what are research disciplines

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Scientific research across and beyond disciplines

Fulvio mazzocchi.

1 CNR—Institute for the Conservation and Valorization of Cultural Heritage, Monterotondo, Italy

The term interdisciplinarity is frequently used to describe the nature of new research fields. But it is not always clear what these terms mean and whether new research fields do fulfill the criteria for truly interdisciplinary research.

Contemporary research is increasingly characterized by two contrasting trends 1 . One is a process of increasing and continuous specialization, which requires scientists to attain a congruent degree of expertise in a particular area of research. This trend is reflected in the proliferation of new scientific disciplines, and their further division into subfields. The other trend, which developed over the past decades, is increasing cooperation not only at an intradisciplinary level, but also across and beyond disciplines: that is, multi‐, inter‐ and trans‐disciplinary research. The aim is to bring together scientists with different expertise and resources, with the possibility of cross‐fertilizing each other and to develop new, synthetic views.

The need for involving several different disciplines arose as scientists realized that particular problems are too complex to be effectively addressed by a single field of study. An obvious example is climate change along with environmental challenges, sustainable development and the societal implications. It requires the competencies and tools from multiple disciplines—natural sciences, engineering and social sciences—to study the causes and effects and develop solutions.

It is also recognized that many systems or phenomena can and should be investigated at different levels and from different points of view, given their multidimensional nature. Take for example human beings, which can be referred to as physical, chemical, biological, cognitive, and sociocultural objects 2 . Each level of organization raises specific issues that should be studied through appropriate strategies and methods, along with the interactions between different levels. Generally, instead of being disciplinary oriented, another way of conceiving scientific investigation is phenomenon‐ or object of study‐oriented.

Multidisciplinarity and interdisciplinarity have also become important for research policy, as exemplified by European Research Council's initiatives, and numerous areas of study, including science education and research management. Various research institutions around the world, such as the Santa Fe Institute, which has no permanent faculty or departments, were also created with the explicit purpose of overcoming the limitations of the academic organization into distinct disciplines.

Interdisciplinarity intrinsically depends on disciplinary knowledge as a prerequisite even if it is a response to the shortcomings of the disciplinary organization.

What does not help the development of interdisciplinary research are exaggerated, rhetorical claims, about its presumed liberating and innovative nature versus the constraints and conservatism of disciplinarity. Interdisciplinarity intrinsically depends on disciplinary knowledge as a prerequisite even if it is a response to the shortcomings of the disciplinary organization.

Disciplinarity and its limits

The disciplinary organization of scientific knowledge and practice became a central element during the 1960s, when philosophy of science highlighted the importance of evaluating scientific theories and ideas as embedded in their own historical context. Leading scholars in this field portrayed the advance of science in terms of “normal science”—science under the guidance of a paradigm—and “revolutionary science”—paradigm‐changing science—(Thomas Kuhn's The Structure of Scientific Revolutions [1962]), progressive and degenerative research programmes (Imre Lakatos's History of Science and its Rational Reconstruction [1979]) or as successive research traditions (Larry Laudan's Progress and its Problems. Towards a Theory of Scientific Growth [1977]).

In a disciplinary framework, scientists tend to share a vocabulary, along with a set of basic epistemic means and commitments, depending on the training that has formed them—here, the term “epistemic” means “relating to knowledge or the conditions for acquiring it”. This training is organized in such a way to enable apprentices to progressively specialize to become experts, so that they can employ their methods even in new contexts. Accordingly, there are two distinct but related ways in which a scientific discipline or specialty can be described: as an epistemic structure, a shared set of cognitive devices—theories, methods, exemplary problem solutions—like those used in molecular biology; or as a social structure, a scholarly community like that of molecular biologists, who makes use of these devices and further refines them 1 .

Disciplinary research has been and will be extraordinary effective in ensuring scientific and technological advancement. On the other hand, as argued by the Spanish philosopher José Ortega y Gasset, a possible side effect of the ever‐growing specialization is the narrowing of intellectual horizons and the creation of what he called “learned ignorami”: people who are experts in their own particular area, but not capable to see beyond. The French sociologist Edgar Morin similarly has criticized the ensuing fragmentation of knowledge and hyperspecialization, which are linked to a reductionist way of thinking that has had a deep influence on how we organize knowledge and educational systems.

…the limits of the disciplinary organization of knowledge are also revealed by an increasing appeal to alternative approaches, namely multidisciplinarity, interdisciplinarity, and transdisciplinarity.

At any rate, Kuhn makes a distinction between normal science, which is mostly analytical and involves a continuous work of articulation of the dominant paradigm, and scientific revolutions, which involve large‐scale, holistic changes of how a particular scientific area is understood. Kuhn also mentions other ways in which the development of science takes place, for instance, by combining two fields as in the case of biochemistry. Nevertheless, his main focus, still reflected in today's prevailing approach of the philosophy of science, is the dynamics of individual disciplines. As a result, what occurs across disciplinary boundaries—or also within disciplines owing to internal fragmentation that may also lead to the creation of new branches—has not received enough attention yet. According to Morin, the history of science cannot be limited to the story of creation, evolution and proliferation of individual disciplines, but should take into consideration the moments when disciplinary boundaries were overcome. Scientific disciplines are not, in fact, totally enclosed and separate islands of knowledge with immutable borders as migration of notions and methods across disciplines constantly takes place.

Take the case of molecular biology. Its rise could not have happened without contacts and transfers between disciplines at the edge of physics, chemistry and biology. The quantum physicist Erwin Schrödinger with his book entitled What Is Life? (1944) was greatly influential in inspiring pioneers scientists, who were involved in the rise of molecular biology during the 1950s. Many of them were physicists like Max Delbrück, a distinguished student of Niels Bohr. During that period, theoretical physics played a crucial role in the development of new directions in biology, and new analytical techniques were derived from biophysics and biochemistry.

Hybridization is today seen as an increasingly important feature of knowledge production, even with the creation of second‐generation hybrid disciplines, especially in the natural sciences. An example is neuroendocrinology, which is a hybrid of endocrinology and neurophysiology 2 .

These facts question traditional metaphors and images of knowledge, for instance, a tree with different branches as represented by Francis Bacon in the 16th century, which emphasize the foundation and unity of knowledge and science. Owing to its growing complexity, the way knowledge is represented makes use of nonlinear images, such as the rhizome, which is typified by a dynamic connectivity (any point can be connected to any other), and is not organized around a central root or hierarchical axis but has multiple entryways 2 .

As already mentioned, the limits of the disciplinary organization of knowledge are also revealed by an increasing appeal to alternative approaches, namely multidisciplinarity, interdisciplinarity and transdisciplinarity. What these have in common is that they all aim to relate people who have been trained in distinct disciplinary environments, and have diverse expertise. However, multidisciplinarity, interdisciplinarity and transdisciplinarity use different strategies and usually have diverging purposes and implications.

In multidisciplinarity, different specialists come to investigate a common issue, which is nowadays common in many research projects. An enriched view is gained by using tools and information from multiple disciplines. However, disciplinary boundaries are still maintained. Actually, multidisciplinarity juxtaposes disciplines, combining them in an additive way and with little cross‐fertilization, that is, without an overall framework for integrating the different perspectives. The likely outcome of a multidisciplinary project is a collection of yet separated research strategies and products.

Interdisciplinarity requires more commitment to go beyond disciplinary boundaries. It involves the search of a common ground for different disciplinary contributions and their amalgamation or synthesis into something new. Whereas multidisciplinarity is simply additive, interdisciplinarity recognizes that solutions to particular problems can only be reached by integrating parts of the original disciplines into a broader, more comprehensive framework, even if such an amalgamation or integration might not necessarily be complete.

Whereas multidisciplinarity is simply additive, interdisciplinarity recognizes that solutions to particular problems can only be reached by integrating parts of the original disciplines into a broader, more comprehensive framework ….

Transdisciplinarity involves the formulation of cognitive schemes that cross disciplinary boundaries. It usually seeks a more holistic approach, which in some versions is linked to an attempt to regain some sort of unity of science or to particular readings of complexity theory. However, in other “contextualized” versions, the focus is on joint problem solving, something that still requires more than just juxtaposition: it is the interpenetration of disciplinary epistemologies, which should go hand in hand with the acknowledgement that scientific knowledge cannot be divorced by the social context.

Let us now focus more specifically on interdisciplinarity, although the following considerations may be relevant for the other types of interactions too. These interactions are, however, hampered by obstacles at different levels. For example, the social organization of science and most academic patterns of knowledge production and evaluation are still ingrained in the disciplinary scheme. The situation is reflected in the organization of university departments and their teaching and training programmes. Academic structures are often characterized by conservatism, something that, however, also depends on their commitment to preserve the disciplines’ core and to guarantee proper standards of training and research; academics and researchers are therefore not encouraged to venture too far from the safe ground of the disciplinary borders, sometimes even believing that “real” science is possible only within these borders 3 . An “echo” of such a disciplinary orientation can be found in research funding and reward systems, in the scope of journals, in the modes of peer‐reviewing and quality control 4 and in the standards for evaluating scientific research that prefer normalized citation measures.

…the social organization of science and most academic patterns of knowledge production and evaluation are still ingrained in the disciplinary scheme.

As a result, interdisciplinary research remains underestimated. Young scholars usually regard it as something that risks their career advancement, and genuine interdisciplinary projects or grant proposals are not common. Many grant proposals include interdisciplinarity only superficially, mostly to increase the chance of being funded. One way to improve its appeal is to make substantial structural changes at the institutional and science policy levels, for example, by reforming university's department organization and training programmes to find a balance between maintaining disciplinary core expertise and enabling the creation of synergistic research environments, or by improving peer‐review systems 3 .

Further challenges concern the intellectual and conceptual level. For instance, there are the well‐known difficulties in communicating between specialized fields, such as when the same term is used with different meanings in distinct contexts. Interdisciplinarity is also made difficult by cognitive barriers between disciplines. It is true that all scientists share fundamental principles—such as observation and inferential forms like deduction and induction—together with the basic tenets of modern science, notably relying on experiments. However, it is also true that researchers from different disciplinary backgrounds are likely to embrace dissimilar assumptions, generalizations and models, including those concerning the object of study itself. They may also diverge in their research strategies, methodologies, even reasoning styles. This contrast is, of course, even more evident between researchers from the natural sciences, who implement quantitative and experimentally based method, and value technical precision and predictive power, and researchers from the humanities and social sciences, who make mostly use of qualitative and sociohistorical analyses.

A case study to exemplify these difficulties regards the encounter between experimental social psychologists and ethnographic anthropologists. It concerns recent research supplying experimental evidence to the thesis that there are deep dissimilarities in the thought patterns of people from distinct cultural settings, for example Western and Asian people (as reported in Richard Nisbett's book The Geography of Thought: How Asians and Westerners Think Differently…and Why [2003]). Cultural differences are the raison d’être of anthropology and have been thoroughly investigated in this field, albeit not on an experimental ground, but mostly on ethnographic data. The bone of contention is the suitability of the type of method involved: ethnographic versus experimental. Anthropologists consider experiment at best as unnecessary, but even potentially harmful, since cultural issues cannot be studied under artificial conditions, that is, outside their living environment. Psychologists claim the importance of experimental method even in studying cultural matters, believing that anthropologists are opposed to something without fully understanding it 5 .

A related obstacle to interdisciplinary research depends on preconceptions about the degree of “scientificity” of disciplines. As reported in many cases of projects involving natural scientist and social scientists, there is a clear asymmetry between them. Usually, the role and skills of social scientists are underestimated, together with their possible contribution to the project 6 . More generally speaking, many interdisciplinary projects end up privileging a single disciplinary perspective, relegating others to secondary roles.

The development of science still depends on specialization, and in fields such as physics and chemistry, the organization in disciplines and specialties will likely continue to work very well. However, there are also situations in which disciplinary boundaries hinder or slow down scientific advance. Further progress precisely needs working at the interface of multiple fields of study.

The ability to work at the edge of multiple knowledge domains is what actually typifies many innovative thinkers, who are capable of intuitively establishing connections between apparently unrelated pieces of information.

Divergent thinking can be productive, but on the other hand it requires ways to manage it. A complex process of mutual learning and continuous negotiation helps to bridge and accommodate different disciplinary perspectives and to meaningfully relate their respective methods, concepts and protocols. The degree to which this is possible strongly affects how interdisciplinary research is undertaken and its accomplishment 1 . Scholars engaged in interdisciplinary research therefore should have a genuine appreciation of pluralism in its different forms and at different levels. Recognizing its value involves, for example, admitting the fact that different disciplinary viewpoints may counterbalance or complement each other to get a broader picture of the question at issue.

On the other hand, individual interdisciplinary researchers, who are pressed out their disciplinary boundaries, have to navigate through unexplored territory. The ability to work at the edge of multiple knowledge domains is what actually typifies many innovative thinkers, who are capable of intuitively establishing connections between apparently unrelated pieces of information. It is precisely here where ground‐breaking, unexpected insights could arise: new explanations and solutions to old problems, together with new questions; innovation at the methodological level; new ideas, usually developed by nonlinear mechanisms like analogy or contamination; or new conceptual links.

…different disciplinary viewpoints may counterbalance or complement each other to get a broader picture of the question at issue.

In order to better substantiate these observations, it is important to look at specific case histories. Such an analysis helps to go beyond conceptual distinctions, considering the ambiguity of terminologies that refer to interactions across disciplines. The same research project may be labelled differently depending on the meanings attributed to multidisciplinarity, interdisciplinarity and transdisciplinarity.

Usually interdisciplinarity is understood as strictly linked to the degree of conceptual, theoretical and methodological integration between the disciplines involved. Such an integration is both a basic expectation for its success and a required condition for distinguishing interdisciplinarity from multidisciplinarity. However, in alternative, that is “instrumental” versions of interdisciplinarity, integration is understood on a pragmatic basis, relating to specific purposes and working on a local and temporary basis 7 . Actually, integration risks to be a tricky notion or to function as an abstract ideal. In fact, as a prerequisite, it may be matched in different ways and to different degrees.

Sometimes the integration process may lead to the birth of new interdisciplinary areas, which in time turn into new disciplines. Consider again the origin of molecular biology, which implied intense interdisciplinary exchange. Molecular biology is now an established discipline—a paradigmatic science—and its practitioners are no longer labelled as interdisciplinary. Interdisciplinary research only corresponded to the early days of the field's development: in the beginning, it was carried out by interdisciplinary teams (“collaborative” interdisciplinarity) and then driven by the first molecular biologists (“individual” interdisciplinarity). These individual pioneers were able to fully amalgamate the knowledge spanning from different domains, catalysing the foundation of a completely new discipline and, indeed, of a new view of biology 8 .

This has frequently occurred in the history of science. What initially appears as radical and revolutionary ideas or methods, developed through interactions between researchers from separate disciplines, becomes over time an ordinary part of the disciplinary training of subsequent generations of scientists. If this is the case, interdisciplinary research should not then be valued for its own sake, but for creating the suitable conditions for novel disciplines to emerge: “Perhaps the whole idea of interdisciplinary science is the wrong way to look at what we want to encourage. What we really mean is ‘antedisciplinary’ science—the science that precedes the organization of new disciplines” 8 .

Such a scheme works well in many situations, but not in all. The degree of integration does not establish per se a scale of values, and does not correspond to a necessary evolution. In some cases, the new interdisciplinary fields resulting from a partial merging stabilize over time, and they do not coagulate into a new single discipline, as happened for molecular biology.

Consider, for instance, the case of cognitive sciences, the development of which has also involved multiple disciplines from neuroscience, psychology, artificial intelligence, philosophy, linguistics, anthropology and so on. Still, after a few decades, no amalgamation of these disciplines into a unified cognitive science has been reached. Cognitive sciences still maintain an interdisciplinary character, something that is reflected in the looser institutional organization if compared with traditional disciplines 5 .

One of the fields that benefits in particular from interactions across disciplines is biology, which is in an ongoing process of transformation, both theoretically and technology‐driven. A number of interdisciplinary areas are involved, such as computational biology (which combines knowledge from molecular biology, computer science, statistics and mathematics), synthetic biology (which draws from molecular biology, evolutionary biology, biotechnology, chemical and biological engineering, electrical and computer engineering) and systems biology.

The rise of systems biology has been made possible by molecular biology, but it also corresponds to a broadening of molecular biology's original scope. It involves a shift in the object of study and type of approach. The focus is no longer on studying isolated phenomena one at a time, for example single genes or proteins. A systems approach, instead, investigates the interrelations between multiple pieces of biological information, considering, for example, the pathways and networks underlying cellular function, with the purpose of understanding how the component parts come together to form the living whole.

In some cases, the new interdisciplinary fields resulting from a partial merging stabilize over time, and they do not coagulate into a new single discipline, as happened for molecular biology.

An important contribution to the field's development has come from the omics approach, initiated by the human genome project and other sequencing efforts. Systems biology is, in fact, attempting to integrate comprehensive sets of biological data from various hierarchical levels and explain them by combining formal numerical modelling and computational analysis with large‐scale experimental techniques. In order to achieve such goals, multiple fields of expertise must be coordinated, together with the respective methods and modes of investigation. At present, systems biology is then primarily grounded on collaborative interdisciplinarity, requiring the skills of biologists, physicists, mathematicians, statisticians, computer scientists and engineers.

Many of the questions discussed so far are well exemplified by systems biology, beginning with the difficulties to institutionalize the field with its interdisciplinary features to facing the constraints of academic organization. There is also a need for new conductive research environments to facilitate collaboration between different types of expertise (e.g. theoretical, experimental and computational). Such a collaboration usually requires cohabitation of researchers and appropriate infrastructure and facilities 3 . New dedicated research centres were built with specifically designed spaces to favour cross‐disciplinary and interlaboratory interactions. A few examples of such structures are the Manchester Interdisciplinary Biocentre ( http://www.mib.ac.uk/ ), the Institute for Systems Biology in Seattle (USA; https://systemsbiology.org/ ) and The Systems Biology Institute in Japan ( www.sbi.jp/ ).

However, setting up new institutes is not enough. The barriers that have to broken down depend also on researchers’ attitude towards collaboration. A common risk in these types of interactions is that the role of other specialists is read in the light of some cliché (e.g. computer specialists may be seen as computer jockeys by experimental scientists, and biologists may be seen as laboratory technologists by computer scientists) 9 . What is needed is a disposition towards learning from other specialists and an engagement in processes of mutual discovery, rather than a mere focus on what others should learn from us.

In addition, not all disciplines have equal status in systems biology. Clearly, biology plays a predominant role. Only biological knowledge can provide, for example, the content for abstract mathematical models, as those used for regulatory networks. However, one should avoid to consider skills in mathematical modelling and computer science, as “ancillary” to biology. All the expertise, in their own ways, is actually necessary for more thoroughly addressing the shared question 9 .

In the interdisciplinary framework of systems biology, communication problems due to the lack of a common vocabulary and set of concepts are also well documented. What happens in such situations is that scientists, in order to facilitate communication, have to learn the meanings of other disciplines’ terms and to develop a shared language. Here, a parallel could be made with intercultural interactions (Peter Galison's Image and Logic: A Material Culture of Microphysics [1997]).

In anthropology, two steps in linguistic interactions between different sociocultural groups have been distinguished: in the first step, a “pidgin” language emerges: a basic tool for communication that is usually limited to certain domains and coexists with the mother tongue of each group. In the second step, the pidgin progressively extends to other domains, until a new “creole” emerges from the mix of the languages involved. Such a creole is a full‐fledged language and functions as the often unique mother tongue of the sociocultural groups who have created it and now identify with it.

Comparable situations take place when different scientific groups, which may be analogous to members of different cultures, interact in interdisciplinary ventures like systems biology 9 . It is then possible to follow the development of a field focusing on the corresponding development of its language.

In the case of molecular biology, a new (creole) vocabulary finally emerged, following the transition from collaborative to individual interdisciplinarity and then on to the new discipline. Is, however, the systems biology's case easily readable through this scheme? Will systems biology turn from being a distributed activity, as it is now, into a novel disciplinary field with its own specialized language? Perhaps, but yet there are different opinions about its possible evolution.

Such a discordance is also reflected in the dispute about how to train future system biologists. The most controversial question is whether the training in systems biology should start at the undergraduate level, even if many agree that mathematics and computation should play a more relevant role in undergraduate courses. Perhaps, there will be more researchers in the future, who have expertise in biology, mathematics and computation science. Perhaps, junior scientists, who have been trained in the new systems biology institutes, will become fully grown specialists, equipped with a new vocabulary. However, other scholars think that “this should not even be an aspiration. Instead of establishing a new discipline, maybe those who describe themselves as “systems biologists” in the future will be integrators rather than specialists” 9 . That is to say, as part of their expertise, they will have the ability to facilitate interchange across different fields. If so, disciplinary distinctions and languages will likely continue to exist, and systems biology will then maintain its interdisciplinary feature.

In conclusion, a few words could be said about the role that philosophy of science could play in the further progress of interdisciplinarity. As discussed before, interactions across disciplinary boundaries play an important role in the dynamics of research and scientific creativity. Accordingly, the mechanisms behind interdisciplinary practices are usually understood in the “context of discovery”. What is less developed is a normative assessment of interdisciplinary research—for instance, what procedures should govern its testing and validation 10 . There are, indeed, many complex issues that need to be analysed, specifically to what happens in potential cases of epistemic divergence, as are often common in interdisciplinary research. There could be contrasting explanations of the same issue—for instance, molecular versus higher‐level (e.g. phenotypic) explanations of biological phenomena. In addition, when information and knowledge from different disciplines come together, the generation of data and evidence and their analysis could create challenges. Evidence from one discipline might support theories and results from another discipline, but could also contradict them, and trigger scientific controversies 10 . Philosophy of science, through studying and analysing the dynamics of interdisciplinary research, could provide guidance to avoid the pitfalls and improve its methodological basis.

EMBO Reports (2019) 20 : e47682 [ Google Scholar ]

What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

Table of Contents

What is Research?

Types of research methods, research process: how to conduct research, top 10 best practices for conducting research in 2023.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base.

This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

• Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
• Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
• Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
• Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
• Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
• Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
• Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
• Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
• Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
• Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
• Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
• Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Research serves as a cornerstone for knowledge discovery, innovation, and decision-making. Understanding the various types of research methods is crucial for selecting the most appropriate approach to answer your research questions effectively. This guide delves into the major research methods, their applications, and tips on choosing the best one for your study.

1. Quantitative Research: Unlocking the Power of Numbers

Quantitative research is centered around collecting numerical data and employing statistical techniques to draw conclusions. This type of research is often used to measure variables, identify patterns, and establish causal relationships.

• Purpose: Surveys are utilized to collect data from a large audience to identify trends and generalize findings.
• Method: Employ structured questionnaires with closed-ended questions.
• Example: Businesses conduct customer satisfaction surveys to understand consumer preferences and make informed decisions.
• Experiments:
• Purpose: Experiments are designed to test hypotheses by manipulating variables in a controlled setting.
• Method: Use experimental and control groups to establish cause-and-effect relationships.
• Example: In scientific research, experiments are conducted to evaluate the effectiveness of a new drug treatment.
• Observational Studies:
• Purpose: Observational studies involve watching and recording subjects without interference, providing insights into natural behaviors.
• Method: Systematically observe and document phenomena.
• Example: Wildlife researchers use observational studies to study animal behaviors in their natural habitats.
• Secondary Data Analysis:
• Purpose: Re-analyze existing datasets to extract new insights, saving time and resources.
• Method: Utilize pre-existing data from sources such as government databases or academic publications.
• Example: Economists analyze census data to examine employment trends and economic growth.

2. Qualitative Research: Exploring the Depths of Human Experience

Qualitative research focuses on understanding the intricacies of human experiences, beliefs, and social phenomena. It provides rich, in-depth insights and interpretations that numbers alone cannot capture.

• Interviews:
• Purpose: Conduct in-depth interviews to explore individual perspectives and gain insights into complex topics.
• Method: Use semi-structured or unstructured interviews to allow participants to share their thoughts freely.
• Example: Healthcare researchers interview patients to understand their experiences and emotional responses to treatments.
• Focus Groups:
• Purpose: Gather diverse opinions and insights from group discussions on specific topics.
• Method: Facilitate guided conversations with selected participants.
• Example: Marketing teams conduct focus groups to test new product concepts and gather feedback.
• Ethnography:
• Purpose: Immerse in a culture or community to understand their practices, values, and social dynamics.
• Method: Engage in long-term observation and interaction within the community.
• Example: Anthropologists conduct ethnographic research to study cultural rituals and traditions.
• Case Studies:
• Purpose: Provide an in-depth examination of a single subject, event, or organization to uncover insights and identify patterns.
• Method: Use multiple data sources to gain comprehensive knowledge.
• Example: Business analysts study successful startups to identify strategies for growth and innovation.

3. Mixed-Methods Research: Bridging the Gap

Mixed-methods research combines qualitative and quantitative approaches to gain a deeper insight into complex problems. This integration allows researchers to benefit from both numerical data and narrative insights.

• Purpose: Leverage the strengths of both quantitative and qualitative data.
• Method: Employ a combination of surveys, interviews, and other techniques.
• Example: Educational researchers use mixed methods to evaluate student performance through test scores and personal interviews.

4. Cross-Sectional Studies: Snapshot of a Moment

Cross-sectional studies analyze data from a population at a specific point in time to identify patterns, correlations, or differences between variables.

• Purpose: Provide a snapshot of a population’s characteristics and relationships.
• Method: Collect data simultaneously from multiple subjects.
• Example: Public health researchers conduct cross-sectional studies to assess disease prevalence in a community.

5. Longitudinal Studies: Observing Change Over Time

Longitudinal studies track the same subjects over an extended period, providing valuable insights into changes, trends, and long-term effects.

• Purpose: Examine changes and developments over time.
• Method: Collect data from the same participants at multiple intervals.
• Example: Psychologists conduct longitudinal studies to understand cognitive development from childhood to adulthood.

6. Action Research: Solving Real-World Problems

Action research involves collaboration with stakeholders to identify and address practical issues, aiming for immediate impact and improvement.

• Purpose: Implement solutions and drive change in real-world settings.
• Method: Engage participants actively in the research process.
• Example: Educators conduct action research to enhance teaching methods and student engagement.

7. Case-Control Studies: Uncovering Causes and Risks

Case-control studies compare individuals with a particular outcome (cases) to those without it (controls) to identify potential causes or risk factors.

• Purpose: Identify factors linked to specific outcomes or diseases.
• Method: Analyze historical data between cases and controls.
• Example: Epidemiologists conduct case-control studies to investigate potential causes of rare diseases.

8. Descriptive Research: Painting a Picture

Descriptive research aims to provide detailed descriptions and summaries of phenomena without manipulating variables, offering a clear picture of a subject.

• Purpose: Describe characteristics, behaviors, or patterns.
• Method: Use surveys, observations, or case studies.
• Example: Sociologists use descriptive research to document urban population demographics.

9. Correlational Research: Understanding Relationships

Correlational research examines the relationship between two or more variables to identify patterns, associations, or correlations without inferring causation.

• Purpose: Identify patterns and associations between variables.
• Method: Use statistical analysis to determine correlation coefficients.
• Example: Researchers study the correlation between physical activity levels and mental well-being.

10. Grounded Theory: Building Theories from Data

Grounded theory is an approach where theories are developed based on systematically gathered and analyzed data, allowing concepts and frameworks to emerge organically.

• Purpose: Develop theories grounded in empirical evidence.
• Method: Use iterative data collection and analysis.
• Example: Social scientists build theories on workplace motivation through employee interviews and observations.

11. Surveys and Questionnaires: Collecting Direct Feedback

Surveys and questionnaires are structured tools used to collect specific information directly from a target population, providing valuable data for various purposes.

• Purpose: Gather targeted data and opinions from respondents.
• Method: Administer standardized questions to a sample population.
• Example: Market researchers use surveys to gather feedback on consumer preferences and trends.

12. Meta-Analysis: Synthesizing Evidence

Meta-analysis is a powerful statistical technique that combines the results of multiple studies on a similar topic to draw robust conclusions and insights.

• Purpose: Synthesize existing research findings for stronger conclusions.
• Method: Aggregate and analyze data from numerous studies.
• Example: Medical researchers perform meta-analysis to assess the overall effectiveness of treatment across multiple clinical trials.

Choosing the Right Research Method

Selecting the appropriate research method is crucial for achieving valid and reliable results. Consider the following factors when deciding on a research approach:

• Research Objectives: Clearly define your goals and questions to guide method selection.
• Data Type: Determine whether you need quantitative, qualitative, or mixed-methods data.
• Resources: Evaluate available time, budget, and technology.
• Ethical Considerations: Ensure compliance with ethical standards in data collection and analysis.

By understanding these diverse research methodologies and strategically employing best practices, researchers can effectively communicate their findings and contribute to the broader field of knowledge.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

• Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
• Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
• Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

• Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
• Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
• Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

• Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
• Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

• Implement robust data management practices to maintain the integrity and security of research data.
• Transparently document data analysis procedures, including software and statistical methods used.
• Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

• Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
• Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

• Be aware of potential biases in the research process and take steps to minimize them.
• Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

• Seek peer review from experts in your field before publishing or presenting your research findings.
• Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

• Strive to make your research findings replicable, allowing other researchers to validate your results independently.
• Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

• Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

• Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
• Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

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Lib basics: the research process.

• The Keys To Success
• Formulate a Research Question
• Concept Mapping
• Search Strategy

Examples of Academic Disciplines

Academic disciplines.

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For some examples, review the URI Academics page " Schools, Departments, and Programs ." All of these fields of knowledge fall into one of the three major disciplines:

• Social Sciences

To help get you started on the research process, it will help to clarify how the major areas of knowledge are defined, especially in the beginning stages when you are not quite sure of the terms that are used in the literature. It will help when you begin to gather information by making it easier to identify some broad-based sources like encyclopedias. Humanities “The branches of learning (as philosophy, arts, or languages) that investigate human constructs and concerns as opposed to natural processes (as in physics or chemistry) and social relations (as in anthropology or economics)” Social Sciences “A science (as economics or political science) dealing with a particular phase or aspect of human society.” Sciences “Knowledge or a system of knowledge covering general truths or the operation of general laws especially as obtained and tested through scientific method; such knowledge or such a system of knowledge concerned with the physical world and its phenomena.”

The above definitions are from Merriam-Webster's Online Dictionary

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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• Published: 12 November 2019

Interdisciplinarity revisited: evidence for research impact and dynamism

• Keisuke Okamura   ORCID: orcid.org/0000-0002-0988-6392 1 , 2  

Palgrave Communications volume  5 , Article number:  141 ( 2019 ) Cite this article

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Addressing many of the world’s contemporary challenges requires a multifaceted and integrated approach, and interdisciplinary research (IDR) has become increasingly central to both academic interest and government science policies. Although higher interdisciplinarity is then often assumed to be associated with higher research impact, there has been little solid scientific evidence supporting this assumption. Here, we provide verifiable evidence that interdisciplinarity is statistically significantly and positively associated with research impact by focusing on highly cited paper clusters known as the research fronts (RFs). Interdisciplinarity is uniquely operationalised as the effective number of distinct disciplines involved in the RF, computed from the relative abundance of disciplines and the affinity between disciplines, where all natural sciences are classified into eight disciplines. The result of a multiple regression analysis ( n  = 2,560) showed that an increase by one in the effective number of disciplines was associated with an approximately 20% increase in the research impact, which was defined as a field-normalised citation-based measure. A new visualisation technique was then applied to identify the research areas in which high-impact IDR is underway and to investigate its evolution over time and across disciplines. Collectively, this work establishes a new framework for understanding the nature and dynamism of IDR in relation to existing disciplines and its relevance to science policymaking.

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The relationship between interdisciplinarity and citation impact—a novel perspective on citation accumulation

Leading countries in global science increasingly receive more citations than other countries doing similar research

Quantifying progress in research topics across nations

Introduction: a new testbed for evaluating interdisciplinary research.

Many of the world’s contemporary challenges are inherently complex and cannot be addressed or resolved by any single discipline, requiring a multifaceted and integrated approach across disciplines (Gibbons et al., 1994 ; Frodeman et al., 2010 ; Aldrich, 2014 ; Ledford, 2015 ). Given the widespread recognition today that cross-disciplinary communication and collaboration are necessary to not only pursue a curiosity-driven quest for fundamental knowledge but also address complex socioeconomic issues, interdisciplinary research (IDR) has become increasingly central to both academic interest and government science policies (Jacobs and Frickel, 2009 ; Roco et al., 2013 ; NRC, 2014 ; Allmendinger, 2015 ; Van Noorden, 2015 ; Davé et al., 2016b ; Wernli and Darbellay, 2016 ). Accordingly, various national and international programmes, focusing especially on promoting IDR, have recently been launched and developed in many countries through specialised research funding and grants or through staff allocations (e.g., Davé et al., 2016a ; Gleed and Marchant, 2016 ; Kuroki and Ukawa, 2017 ; NSF, 2019 ).

Driving these pro-IDR policies and the attendant rhetoric is an implicit assumption that IDR is inherently beneficial and has a more substantial impact compared with traditional disciplinary research. However, this assumption has rarely been supported by solid scientific evidence, and in most cases, the supposed merit of IDR has been based on anecdotal evidence from specific narrative examples or case studies (for related perspectives, see e.g., Jacobs and Frickel, 2009 , p. 60; Weingart, 2010 , p. 12). Considering the fact that significant resources have been and are being invested in promoting IDR, better clarity regarding the relationship between interdisciplinarity and its potential benefit, particularly the research performance, could help increase accountability for such policy actions.

Extant literature has investigated the relationship between interdisciplinarity and the research performance by using various data sources and methodologies, with different operationalisation of both dimensions (e.g., Steele and Stier, 2000 ; Rinia et al., 2001 ; Rinia et al., 2002 ; Adams et al., 2007 ; Levitt and Thelwall, 2008 ; Larivière and Gingras, 2010 ; Chen et al., 2015 ; Elsevier, 2015 ; Yegros-Yegros et al., 2015 ; Leahey et al., 2017 ). Owing to such diverse investigation approaches, it is unsurprising that the results are usually neither consistent nor conformable and sometimes are even contradictory among the literature. Given this situation, it is desirable that a more robust and reproducible methodology be developed and implemented to systematically assess the value of IDR in practice. The present study seeks to contribute to this goal by developing a new testbed for IDR evaluation. The focus is especially placed on highly cited paper clusters known as the research fronts (RFs), which are defined by a co-citation clustering method (Small, 1973 ). In this new approach, the research interdisciplinarity is characterised by the disciplinary diversity of the papers that compose the RF, and the research performance is operationalised and measured as a field-normalised citation-based measure at the RF level.

This proposed RF-based approach has three major advantages over common approaches that focus, for instance, on individual papers (Steele and Stier, 2000 ; Adams et al., 2007 ; Larivière and Gingras, 2010 ; Chen et al., 2015 ; Elsevier, 2015 ; Yegros-Yegros et al., 2015 ) to investigate the potential effect of interdisciplinarity on high-impact research. First, through the analyses of RFs, it is possible to capture a snapshot of the most lively, animated and high-impact research currently being undertaken in the academic sphere, since the papers composing RFs are classified as the most highly cited papers for each science discipline. As science policymakers, leaders, funders and practitioners are often most interested in promoting and supporting high-impact research, the evidence and insights obtained through this investigation of RFs can assist them in formulating more accountable policy recommendations that otherwise cannot be adequately addressed. Second, the RF is a unique manifestation of knowledge integration from different science disciplines. By construction, the interdisciplinarity operationalised at the RF level does not represent a mere parallel existence of discrete knowledge sources from multiple disciplines; rather, it indicates the state of the knowledge integration from multiple disciplines to create new knowledge syntheses. This organic scientific knowledge structure can be captured more effectively and robustly through RFs than through, for instance, an individual paper’s reference list. Consequently, the emergence of a new high-impact research area will also be more reliably detected at the RF level than at the paper level. The third advantage of the proposed RF-based approach is related to the technicalities. As discussed, RFs are unique self-organised units of knowledge in which bibliographically important information is effectively compressed and integrated. As this study considers thousands of papers, it is considerably more efficient and effective to handle RFs compared with a multitude of papers while conducting data retrieval, analysis and visualisation. These multifold advantages of the RF-based approach enable this study to comprehensively and uniquely assess the value of interdisciplinarity.

Methods: through the lens of emergent research fronts

The analyses in this study were based on the data retrieved from the Essential Science Indicators (ESI) database, published by Clarivate Analytics, and data published by the National Institute of Science and Technology Policy (NISTEP) of Japan. In this section, the definitions for the main terms used in this paper—the RFs, the research areas, the research impact and the interdisciplinarity index—are provided. Subsequently, the regression model specification used in this study and the rationale behind it are detailed.

Research fronts and (broad) research areas

The bibliometric data for the research papers (regular scientific articles and review articles) and citation counts were derived from more than 10,000 journals indexed in the Web of Science Core Collection published by Clarivate Analytics. The master journal list is updated regularly, with each journal being assigned to only one of the 22 ESI research areas (see Supplementary Table S1 ). Given a pre-set co-citation threshold, the original ‘ESI-RFs’ were defined based on the number of times the pairs of papers had been co-cited by the specified year and month within a five-year to six-year period. The ESI-RF investigation in this paper was focused on papers classified as ‘Highly Cited Papers’ in the ESI database, which are the top 1% for annual citation counts in each of the 22 ESI research areas based on the 10 most recent publication years.

Based on the ESI framework, the NISTEP’s Science Map dataset (NISTEP, 2014 , 2016 , 2018 ) defines a set of ‘aggregate RFs’ using a second-stage clustering in each of the three data periods: 2007–2012, 2009–2014 and 2011–2016, which are denoted in this study as S 2012 , S 2014 and S 2016 , respectively. Each dataset comprised approximately 800–900 of such ‘aggregate RFs’ (hereinafter referred to as ‘RFs’). The i -th RF in the aggregate dataset S   =   S 2012   ∪   S 2014   ∪   S 2016 was denoted by RF i . After excluding two RFs with missing data, there were | S | = 2,560 RFs collected for the total data period (2007–2016), with a cumulative number of 53,885 papers (Table 1 ).

For this study’s purpose, the 22 ESI research areas were reorganised into nine broad categories based on the classification scheme in Supplementary Table S1 . Of these, we focused on the following eight categories composed of 19 ESI natural science areas: ‘ Environmental and Geosciences ’, ‘ Physics and Space Sciences ’, ‘ Computational Science and Mathematics ’, ‘ Engineering ’, ‘ Materials Science ’, ‘ Chemistry ’, ‘ Clinical Medicine ’ and ‘ Basic Life Sciences ’, which we denote collectively as \({\mathscr{R}}\) . The other category, composed of the three ESI ‘non-natural-science’ areas—‘ Economics and Business ’, ‘ Social Sciences, General ’ and ‘ Multidisciplinary ’—was excluded from the analyses because the main research output were books rather than journal papers and thus were under-represented in the data.

Research impact measure

Although higher citations do not necessarily represent the intrinsic value or quality of a paper, research impact is commonly operationalised as citation-based measure (e.g., Steele and Stier, 2000 ; Rinia et al., 2001 , 2002 ; Adams et al., 2007 ; Levitt and Thelwall, 2008 ; Larivière and Gingras, 2010 ; Chen et al., 2015 ; Elsevier, 2015 ; Yegros-Yegros et al., 2015 ), which is due to not only its intuitive and computational simplicity but also the data availability and tractability. Moreover, the citation-based research impact is often defined as a field-normalised measure, that is, the absolute citation counts divided by the world average in each discipline, in order to take into account for the disciplinary variations in publication and citation practices. This study also used a surrogate field-normalised citation-based measure of research impact; however, in contrast to previous studies, it was defined and measured at the RF level rather than at a paper level (Steele and Stier, 2000 ; Adams et al., 2007 ; Larivière and Gingras, 2010 ; Chen et al., 2015 ; Elsevier, 2015 ; Yegros-Yegros et al., 2015 ), at a journal level (Levitt and Thelwall, 2008 ) or at a research programme level (Rinia et al., 2001 , 2002 ).

Let N i be the number of papers comprising RF i , and let \(N_i = \mathop {\sum}\nolimits_{{\mathrm{A}} \in {\mathscr{R}}} {N_{i,{\mathrm{A}}}}\) be its decomposition based on the research areas, where N i ,A is the number of papers in RF i attributed to each research area A  ∈   \({\mathscr{R}}\) . Let X i be the actual citation counts received by RF i . Let also C A;y/m be the baseline citation rate for each research area A as noted on the ESI database as of the specified year and month (‘y/m’), which is defined as the total citation counts received by all papers attributed to research area A divided by the total number of papers attributed to the same research area in the 10 years of the Web of Science. Then, the mean baseline citation rate for each research area A, denoted 〈 C A 〉, was obtained by averaging C A;y/m over all the ESI data periods from March 2017 to January 2019 (i.e., from y/m = 2017/03 to 2019/01; bimonthly) (Supplementary Table S2 ). Subsequently, the research impact measure for RF i was defined by

that is, the ratio of the actual citation counts earned by RF i to the expectation value of the citation counts for the same RF.

Interdisciplinarity index

The context-dependent nature of research interdisciplinarity has made its identification and assessment far from trivial, hitherto without a broad consensus on its operationalisation (Porter and Chubin, 1985 ; Morillo et al., 2003 ; Huutoniemi et al., 2010 ; Klein et al., 2010 ; Wagner et al. 2011 ; Siedlok and Hibbert, 2014 ; Adams et al., 2016 ). Numerous attempts have been made to develop methodologies for operationalising interdisciplinarity in practice, not only at the paper level (Morillo et al., 2001 ; Adams et al., 2007 ; Porter and Rafols, 2009 ; Larivière and Gingras, 2010 ; Chen et al., 2015 ; Elsevier, 2015 ; Yegros-Yegros et al., 2015 ; Leahey et al., 2017 ) but also at a journal level (Morillo et al., 2003 ; Levitt and Thelwall, 2008 ; Leydesdorff and Rafols, 2011 ) or at a research programme level (Rinia et al., 2001 ; Rinia et al., 2002 ). Still, it is most popularly defined at a paper level, either in terms of ‘knowledge integration’, as measured through the proportion of references from different disciplines, or ‘knowledge diffusion’, as measured through the proportion of citations received from different disciplines (Porter and Chubin, 1985 ; Adams et al., 2007 ; Van Noorden, 2015 ). Regardless of the operationalisation level, a more refined quantitative approach to interdisciplinarity, conceptualised as the disciplinary diversity, necessarily requires the following three aspects: ‘variety’ (number of disciplines involved), ‘balance’ (distribution evenness across disciplines) and ‘dissimilarity’ (degree of dissimilarity between the disciplines) (see Rao, 1982 ; Stirling, 2007 ). Most previous IDR studies have evaluated interdisciplinarity based on either variety or balance, while some recent studies (e.g., Porter and Rafols, 2009 ; Leydesdorff and Rafols, 2011 ; Mugabushaka et al., 2016 ) have made efforts to incorporate the aspect of dissimilarity as well.

This study also operationalises interdisciplinarity as an integrated measure of the aforementioned three aspects; however, in contrast to previous studies, it was uniquely operationalised at the RF level. Specifically, the interdisciplinarity index for RF i was defined and evaluated using the following ‘canonical’ formula (Okamura, 2018 ):

Here, w i ,A denotes the relative abundance of a research area A in RF i , defined by, using the previous notations, w i ,A  =  N i ,A / N i , satisfying \({\sum\nolimits_{{\mathrm{A}} \in {\mathscr{R}}}} {w_{i,{\mathrm{A}}} = 1}\) . The effective affinity (i.e., similarity) between each pair of research areas A and B in \({\mathscr{R}}\) , denoted 〈 M AB 〉 in (2), was defined as the time-averaged Jaccard indices (see Supplementary Methods and Discussion ), where, as before, the bracket ‘〈…〉’ represented the average over the 12 ESI data periods. Figure 1 shows the chord diagram representation of the affinity matrix (see Supplementary Table S3 for the source data), from which it was evident that the degree of affinity varied considerably for different pairs of the disciplines.

A chord diagram representation of the affinities between research areas. The affinity indices were defined as the time-averaged Jaccard similarity indices and were evaluated between each pair of research areas ( Supplementary Methods and Discussion ). They were assigned to each connection between the research areas, represented proportionally by the size of each arc, from which it is evident that the degree of affinity varied considerably for different pairs of the disciplines (see Supplementary Table S3 for the source data)

The interdisciplinarity index (2) is unique because it is conceptualised as the effective number of distinct disciplines involved in each RF and is robust regarding the research discipline classification scheme. Specifically, it has the special property of remaining invariant under an arbitrary grouping of the constituent disciplines, given that the between-discipline affinity is properly defined for all pairs of disciplines. For instance, suppose one is interested in measuring the interdisciplinarity of RF i based on the classification scheme \({\mathscr{R}}\) 1 and someone else wishes to measure the interdisciplinarity of the same RF i based on the more aggregate classification scheme \({\mathscr{R}}\) 2 . Then, for the interdisciplinarity index to be a consistent measure of disciplinary diversity, both approaches must result in the same value for the interdisciplinarity; that is, \({\it{\Delta }}_i\left[ {{\mathscr{R}}_1} \right] = {\it{\Delta }}_i\left[ {{\mathscr{R}}_2} \right]\) . Otherwise, it results in an inconsistent situation as the interdisciplinarity changes with respect to the level (or ‘granularity’) of the research discipline classification, while the physical content of the RF (i.e., the constituent papers) remains the same. Note that popular (dis)similarity-based diversity measures such as the Rao-Stirling index (Rao, 1982 ; Stirling, 2007 ) and the Leinster-Cobbold index (Leinster and Cobbold, 2012 ) do not generally satisfy this invariance property; to the best of our knowledge, the only known diversity measure that respects this invariance property is given by the formula (2), the theoretical grounds for which have recently been established for a general diversity/entropy quantification context (Okamura, 2018 ).

Using this formula, the interdisciplinarity index for each RF in S was obtained, from which it was found that 43.6% of the RFs were mono-disciplinary (i.e., Δ = 1) and more than half were interdisciplinary (Fig. 2a ; median = 1.2, range = 2.5; see also Supplementary Fig. S1a ).

Relationship between research impact and interdisciplinarity. a The histogram for the interdisciplinarity index (median = 1.2, range = 2.5, interquartile range = 0.58); b The histogram for the log-transformed research impact (mean = 1.2, SD = 0.83); c The scatterplot showing the associations between the interdisciplinarity index and the log-transformed research impact. The solid line in the scatterplot represents the robust linear model fit. The shaded region and the dashed lines, respectively, indicate the 95% confidence interval based on the standard error of the mean and on the standard error of the forecast, including both the uncertainty of the mean prediction and the residual

Regression model

Based on the aforementioned operationalisations of the research impact and the interdisciplinarity index, the relationship between the two variables was analysed using a regression analysis method. As the histogram analysis showed that the original research impact distribution was skewed, it was log-transformed so that the distribution curve was closer to a normal curve (Fig. 2b ; mean = 1.2, SD = 0.83; see also Supplementary Fig. S1b ). The scatterplot of the log-transformed research impact against the interdisciplinarity index indicated that these variables were relatively linearly related (Fig. 2c ; see also Supplementary Fig. S2a–c ). Subsequently, the following multiple linear regression model was investigated:

where, x i was a l ×  k vector for predictive variables, and β was a k  × l vector for the regression coefficients, which were the unknown parameters to be estimated (with k being some integer). To deal with the possible issue of heteroscedasticity, the model was analysed using heteroscedasticity-robust standard errors (i.e., the Huber-White estimators of variance). In addition, a test for serial correlation (i.e., the Breusch-Godfrey Lagrange multiplier test) was conducted as a post-estimation procedure, which indicated that there was no serial correlation between the residuals in each model considered (see below).

For comparability, five different regression models corresponding to different specifications of the predictive variables were analysed and labelled Models 1–5, with the following sets of predictive variables, respectively, defined for each model:

In Model 1, the interdisciplinarity index was used as the only predictive variable, which was added to the intercept term (constant). In Model 2, the variables associated with IntlCollab and IntlCiting , denoting the proportion of internationally collaborated papers in papers comprising an RF and in the citing papers, respectively, were included as additional predictive variables. Models 3, 4 and 5, in the same manner, represented the prior model with a new set of predictive variables, respectively, added as follows: Year dummy variables for the different years (2012, 2014 and 2016) of the Science Map to capture the possible time-fixed effects; a ‘ Research Area ’ control set to represent the proportion of papers belonging to each research area A  ∈   \({\mathscr{R}}\) ; and a ‘ Country ’ control set to represent the proportion of papers for which authors from each country of \({\mathscr{C}}\)  = { US, France, UK, Germany, Japan, South Korea, China } contributed (measured on a fractional-count basis). The last two control sets were introduced to, respectively, account for the possible discipline-related and country-related effects that could reflect such factors as research environment, practices and cultures intrinsic to each discipline or/and country.

In interpreting the regression results, each regression coefficient β k (i.e., the k -th component of β in Eq. ( 3 )) indicated that a one point increase in the predictive variable x k was associated with β k point increase in ln( I ), or equivalently, [exp( β k )−1] × 100% increase in the research impact ( I ) at the specified significance level. Care should be taken in interpreting the results for the proportion variables ( IntlCollab , IntlCiting , ‘ Research Area ’ and ‘ Country ’ control sets) as the regression coefficients for each of these variables represented the effect on the criterion variable (i.e., the log-transformed research impact) associated with a 100% increase in the proportion variable. For the time-fixed effects, the base category was chosen as Year  = 2014, against which the effects of the other two data periods (corresponding to Year  = 2012 and 2016) were measured. For the ‘ Research Area ’ control set, the effect of the proportion of each research area in \({\mathscr{R}}\) was measured against the set of ‘residual’ (i.e., ‘non-natural-science’) ESI research areas. Finally, for the ‘ Country ’ control set, the effect of the share of each country in \({\mathscr{C}}\) was measured against the set of those countries not listed in \({\mathscr{C}}\) .

Results: interdisciplinarity as a key driver of impact at research fronts

The results of the multiple regression analyses for all the five models ( n  = 2,560; two-tailed) are summarised in Supplementary Table S4 . Based on the adjusted- R 2 for each model (the bottom row of the table), Model 5 was found to be the preferred model in terms of the goodness-of-fit, and therefore, this model was considered in detail in this study; see Table 2 for the summary table.

Particularly, the estimated coefficient for the interdisciplinarity index was found to be positive and statistically highly significant. Specifically, a one point increase in the interdisciplinarity index in an RF (i.e., an increase in the effective number of distinct disciplines by one) is, on average, associated with approximately a (( e 0.186 −1) × 100% ≈) 20% increase in the research impact, holding other relevant factors constant ( P  < 0.001). This appears to imply that, on average, a high-impact RF is more likely to be formed either in the presence of disciplines that are more dissimilar or with a more balanced mix of distinct disciplines, or both. What this indicates is that while the papers composing the RFs were already high-impact papers as they were classified as ‘Highly Cited Papers’ in the ESI database, nevertheless the degree of the ‘high-impact’ at the RF level was found to be higher on average as the interdisciplinarity level increased. Notably, this implication was found to hold sufficiently generally, reproducing the same results qualitatively for each data period separately (Supplementary Fig. S2a–c ).

Though outside the main scope of the present study, the regression results led to additional intriguing implications for the research impact predictors. Particularly, the regression coefficient for IntlCollab implied that a 1% increase in the international collaboration in an RF was, on average, associated with an approximately 0.6% increase in the research impact ( P  < 0.001), which was also found to hold sufficiently generally across the three data periods. By contrast, the regression coefficient for IntlCiting was found to be negatively significant ( P  < 0.001). For the time-fixed effects, the research impact was found to be, on average, statistically significantly lower in the ‘2012’ data compared with the ‘2014’ or ‘2016’ data ( P  < 0.001). However, no statistically significant difference was observed between the ‘2014’ and ‘2016’ data (see also Supplementary Fig. S1b , which already indicated this trend via the kernel density estimations for the criterion variable). Further, the coefficient for each of the ‘ Research Area ’ variables was found to be positively significant ( P  < 0.001), indicating that, on average, a paper belonging to either area of \({\mathscr{R}}\) is likely to have a higher research impact compared with a paper attributed to the ‘residual’ (i.e., ‘non-natural-science’) research area. Finally, the result for each of the country-share variables in \({\mathscr{C}}\) provided some intriguing insights into its effect on the research impact. For instance, the result for the variable ‘ US ’ implied that, on average, replacing 1% of the contributions from the ‘residual’ countries with that from the US resulted in an approximately 0.3% increase in the research impact ( P  < 0.001). These observed relationships between the research impact and each predictor variable, along with their policy implications, should be investigated in future studies.

Discussion: evolving landscape of cross-disciplinary research impact

To further enhance our understanding of the relationship between interdisciplinarity and research impact, a more detailed investigation of the finer structures and evolutionary dynamism of high-impact research over time and across disciplines is desirable. For this purpose, we present in the following a new bibliometric visualisation technique and demonstrate its potential use in the study of interdisciplinarity.

‘ Science Landscape ’: a novel bibliometric visualisation approach

Significant efforts have been made to visualise scientific outputs, especially bibliometric data regarding the citation characteristics. Such efforts have been partially successful in displaying the links between and across various research disciplines or subject categories (Small, 1999 ; Boyack et al. 2005 ; Igami and Saka, 2007 ; Leydesdorff and Rafols, 2009 ; Porter and Rafols, 2009 ; Van Noorden, 2015 ; Klavans and Boyack, 2017 ; Elsevier, 2019 ). Each alternative form of ‘science mapping’ has its own merit in particular situations, offering complementary and synergistically beneficial implications not only for a deeper understanding of academic (inter-)disciplinarity but also for policy implementation. To contribute to the evidence-base in this fast-growing and innovative field, here we present a new technique—called the Science Landscape —that visualises research impact and its development patterns in relation to the entire natural science discipline corpus. The same research impact measure and the interdisciplinarity index as used in the previous sections were employed to ensure methodological consistency between the empirical implications drawn from this new visualisation technique and the quantitative evidence already obtained from the regression analyses.

In the Science Landscape diagrams (Fig. 3a–c ), the eight (broad) research areas were arranged along the edge of a circular map, with the angle of each research area being proportional to the number of papers attributed to that research area. Each RF was then mapped onto the circular map for each data period (Supplementary Fig. S3a–c ), so that the distance from the edge to the centre indicated the RF’s interdisciplinarity index; that is, the closer it was to the centre, the greater the degree of interdisciplinarity. The angle around the centre was determined by the disciplinary composition; that is, the closer it was to a particular research area, the higher its share in the disciplinary composition. A similar circular research field frame (27 subject areas) is used in the ‘Wheel of Science’ for Elsevier’s SciVal system based on Scopus data (Klavans and Boyack, 2017 ; Elsevier, 2019 ); however, the objectives and what is mapped and how it is mapped are dissimilar. In particular, the Science Landscape shown here was based on 3D mapping technology, so that the height of each RF i was proportional to the log-transformed research impact, ln( I i ), with the highest (‘over the clouds’) and lowest (‘under the sea’) research impact levels being depicted in red and blue, respectively. Here the heights of the RFs were not added vertically; rather, at each map position, the maximum height value was used to depict the surface of the landscape. The rationale behind this method was that for the current purpose of investigating the cross-disciplinary spectrum of research impact, it was more meaningful and implicative to visualise ‘individually outstanding high-impact RFs’ rather than ‘a number of low-impact RFs additively forming high peaks’.

Dynamic evolution of research impact across disciplines. Corresponding to each data period—2007–2012 ( a ), 2009–2014 ( b ) and 2011–2016 ( c )—the Science Landscape diagrams are shown. The figures on the left show the top views and the figures on the right show the birds-eye views. The eight ‘base’ research areas are arranged along the edge of the circular map, and the angle allocated to each research area is proportional to the number of papers from each discipline. The highest and lowest levels of research impact are depicted in red and blue, respectively

Moreover, each RF’s concrete disciplinary composition was indicated by the direction(s) towards which the RF’s peak tails (see Supplementary Fig. S4 ). For instance, in the Science Landscape for 2009–2014 (Fig. 3b ), there is a high research impact peak ( I  = 100.7) near the centre that has one tail towards ‘ Comp & Math ’ and another tail towards ‘ Basic Life Sciences ’ (the solid square region). In light of the original NISTEP’s Science Map dataset (NISTEP, 2016 ), this peak corresponds to the RF characterised by feature words such as ‘RNA Seq’ and ‘next generation sequencing’. Then, intuitively, this correspondence indicates that during this period, there was a scientific breakthrough related to new sequencing technology that occurred at the intersection of these two disciplines. Further technical and mathematical details including the explicit functional form of the 3D research impact profile are presented in Supplementary Methods and Discussion .

Provided the above encoding, the Science Landscape diagrams (Fig. 3a–c ) clearly illustrate how the shape of interdisciplinarity has changed over the three data periods. It is noticeable that the overall landscape of the research impact has never been static, monolithic nor homogeneous; rather, it evolves dynamically, both over time and across disciplines. One of the most remarkable features can be seen in the northwest of the map (dashed circle region) at the low ivory-white-coloured ‘mountains’ in 2007–2012 (Fig. 3a ), where new high-impact RFs are evolving and developing into a group of yellow-coloured mid-height ‘mountains’ in the years up to 2009–2014 (Fig. 3b ) and towards 2001–2016 (Fig. 3c ). This dynamic research impact growth indicates the increased IDR focus around the region during the data period. Thus, this visualisation can assist identifying where the scientific community’s focus of attention is undergoing a massive change, where high-impact IDR is underway worldwide, and where new knowledge domains are being created. Each landscape appears to represent the superposition of the following two research impact evolutionary patterns; one that has steady, stable or predictable development that accounts for the ‘global’ or ‘evergreen’ structure of the landscape, and the other that represents a breakthrough in science or a discontinuous innovation, induced ‘locally’ in a rather abrupt or unpredictable manner. The challenge of science policy, therefore, is developing ways to address each of these dynamic evolutionary patterns and the mechanism thereof and to promote IDR in a more evidence-based manner with increased accountability for the investments made.

Summary and conclusions: towards evidence-based interdisciplinary science policymaking

This study revisited the classic question as to the degree of influence interdisciplinarity has on research performance by focusing on the highly cited paper clusters known as the RFs. The RF-based approach developed in this paper had several advantages over more traditional approaches based on a paper-level or journal-level analysis. The multifold advantages included: quality-screening, cross-disciplinary knowledge syntheses, structural robustness and effective data handling. Based on data collected from 2,560 RFs from all natural science disciplines that had been published from 2007 to 2016, the potential effect of interdisciplinarity on the research impact was evaluated using a regression analysis. It was found that an increase by one in the effective number of distinct disciplines involved in an RF was statistically highly significantly associated with an approximately 20% increase in the research impact, defined as a field-normalised citation-based measure. These findings provide verifiable evidence for the merits of IDR, shedding new light on the value and impact of crossing disciplinary borders. Further, a new visualisation technique—the Science Landscape —was applied to identify the research areas in which high-impact IDR is underway and to investigate its evolution over time and across disciplines. Collectively, this study established a new framework for understanding the nature and dynamism of IDR in relation to existing disciplines and its relevance to science policymaking.

Validity and limitations

The new conceptual and methodological framework developed to reveal the nature of IDR in this paper would be of interest to a wide range of communities and people involved in research activities. However, as with any bibliometric research, this study also faced various limitations that may have impacted the general validity of the findings, and thus, its practicability in the real policymaking process is necessarily limited. To conclude, some of these key issues and challenges are highlighted.

First, both the regression analysis results and the Science Landscape visualisations should be assessed with caution as they may be highly dependent on the research area classification scheme, which is not unique. Research area specifications other than those used in this study could also have been applied. For instance, a factor-analytical approach (Leydesdorff and Rafols, 2009 ) to identify a ‘better justified’ set of academic disciplines could be useful in providing a more nuanced assessment and understanding of the nature of interdisciplinarity and could possibly have higher robustness and reliability. Moreover, a different research area arrangement along the edge of the circular map would have resulted in different Science Landscape visualisations, and the cross-disciplinary spectrum of research impact might have been more plentiful or profound than observed in this study.

Second, in relation to the first point, the quantification of the affinity between the research areas could have been refined in other acceptable ways. Our rationale behind the definition of the between-discipline affinity based on the Jaccard-index was that papers from closer (i.e., with higher affinity) research areas were more likely to be co-cited, and thus more likely to belong to the same ESI-RF (see Supplementary Methods and Discussion ). In this approach, the affinity matrix was defined solely using the bibliometric method, and therefore its matrix elements may have been more or less biased because of the publication/citation practices of the existing disciplines. Consequently, it may have failed to capture the inherent ‘true’ between-discipline affinities responsible for the ‘true’ interdisciplinarity operationalised at the RF level.

Third, it is unlikely that the regression model specification used in this study included every salient research impact predictor. For example, factors such as the types of research institute, departmental affiliations, individual journal characteristics and funding opportunities (e.g., funding agencies and programmes/fellowships) were not considered in the model owing to their unavailability in the dataset. Moreover, the links between the different scientific specialties irrespective of their academic discipline could have also influenced the research performances. These omitted variables may also have affected the regression results because they may be associated with both the criterion variable (i.e., the research impact) and some predictive variables including the interdisciplinarity index.

Finally, there are inherent limitations in using citation-based methods to evaluate research performance. Combining bibliometric approaches with expert judgements from qualitative perspectives will be favoured to extract the policy implications and recommendations from a wider context. Although the societal impacts of research (see e.g., Bornmann, 2013 ) were beyond the scope of the present work, it is hoped that this study’s findings can be extended to incorporate such societal aspects. In so doing, it is also important to consider not only the benefits but also the costs of IDR (Yegros-Yegros et al., 2015 ; Leahey et al., 2017 ) for interdisciplinary approaches to provide viable policy options for decision-makers.

With further conceptual and methodological improvements, it is hoped that future studies can reveal more about the nature of IDR and its intrinsic academic and/or societal value by overcoming some of the aforementioned limitations. Continued efforts will contribute to the development of the more evidence-based and accountable IDR strategies that will be imperative for addressing, coping with and overcoming contemporary and future challenges of the world.

Data availability

The datasets generated and/or analysed during this study are not currently publicly available, but are available from the corresponding author on reasonable request.

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Acknowledgements

This work was conducted as part of the in-house research activities of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. This work also contributes to the MEXT’s ‘Science for RE-designing Science, Technology and Innovation Policy (SciREX)’ programme, hosted at the National Graduate Institute for Policy Studies (GRIPS), for which the author serves as Policy Liaison Officer. The views and conclusions contained herein are those of the author and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the government of Japan.

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Okamura, K. Interdisciplinarity revisited: evidence for research impact and dynamism. Palgrave Commun 5 , 141 (2019). https://doi.org/10.1057/s41599-019-0352-4

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Ecology is not a dirty word

What is a research discipline we need collaboration, not segregation.

I’ve read two new papers this week that got me thinking about how and why we define ourselves as researchers.

One was this excellent paper led by Brian McGill on why macroecology and macroevolution, once essentially part of a single discipline, need to reconverge as they both have complementary goals. As the authors note, macroecology tends to focus on spatial processes, while macroevolution tends to focus on temporal processes. In reality, both types of processes are linked across scales and influence each other. To address fundamental questions about biodiversity and ecosystem function, we need to consider both together.

This segregation across related disciplines is a real problem that we need to address – we’ve seen it with agricultural science and ecology , freshwater & terrestrial ecology and more…

McGill et al. describe how (macro)ecology & (macro)evolution diverged, explain why they need to get back together, and present some pressing research questions as a path forward. It’s a really good paper and I highly recommend it if you work even remotely close to these disciplines.

The second was this paper led by Rogier Hintzen , which I found underwhelming. From my reading, I felt that its aim was to drive a wedge between ecology and conservation biology, which are both closely-related disciplines with common goals. The language throughout the paper is overtly condescending, and consistently implies that ecology is a less relevant discipline to solve today’s environmental problems, compared to conservation biology.

Disclaimer: I’m an ecologist and I work on conservation-relevant problems. I’m here because I care about nature. Sounds trite, but I think that’s what most ecologists are here for.

And as far as I know, there is no pressing knowledge gap that requires a delineation between ecology and conservation biology. But this is the authors’ hypothesis:

“We hypothesize that ecology’s role in conservation biology has waned and that the vision of a science that applies the latest ecological ideas to solving its pressing problems has faded too.”

‘Waned’ and ‘faded’ both connote derogatory declines in ecology’s relevance to solve our ‘pressing problems’. The paper’s discussion continues on this theme. Ecologists are painted as desperate scientists trying to be relevant to conservation “only to discover that they are not that useful after all” and recent developments “are no more promising”.

This is news to me, as I thought both disciplines essentially had common goals and conservation success stories are essentially based on ecological knowledge. Ecosystem services (disclaimer: one of my main research disciplines ) is a fundamentally ecological concept with fundamentally applied conservation goals.

If this paper was an opinion piece, I’d be less critical. But this is a data paper and the methods simply aren’t suitable to test the hypothesis. A text-mining content analysis of a select group of journals, subjectively chosen as representative of each discipline, is not appropriate to claim that ecological knowledge is not useful to solve conservation problems. The journals chosen aren’t even representative of each discipline*, and it’s simply wrong to conflate a journal’s scope & subjective publication process with the relevance of a whole discipline for solving environmental problems. The authors also seem to think that ‘Ecology’, the discipline, is all about theory and models. It’s not (see here and here ) – these are parts of ecology, but not the whole.

I’ve written about the research niche before. Career success depends on finding a niche, but that niche is built on a combination of substrates. Most researchers work across multiple disciplines and publish across multiple journals. Journals are a publication medium – they have a scope and a subjective editorial process that simply cannot define a discipline’s goals or success rate (whatever that means). I call myself an ecologist, but I work and publish across multiple ‘disciplines’*: ecology, entomology, conservation biology, agricultural science, communication, social science. Does it matter?

Pitting disciplines against each other is not helpful or constructive. We are in unprecedented territory with climate change. The environmental problems we face require collaboration across disciplines, not segregation.

____________________

*The authors choose these journals as representative of each discipline: Ecology (Trends in Ecology & Evolution; American Naturalist; Ecology Letters; Ecology; Oikos; Methods in Ecology & Evolution; Ecography; Biological Conservation; Ecological Applications) Conservation Biology (Conservation Evidence; Oryx; Conservation Biology; Conservation Letters; AMBIO; Conservation & Society; Ecology & Society) I’m an ecologist & I’ve barely published in the ‘ecology’ journals (a book review and a natural history note ), but I’ve published two of my key ‘research niche’ papers in Cons Biol and AMBIO .

© Manu Saunders 2019

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3 thoughts on “ What is a research discipline? We need collaboration, not segregation ”

Hmm. To the extent that the content of conservation journals has moved away from that of ecology journals, wasn’t that kind of inevitable given that conservation biology started out as a very ecology-focused discipline? The amount of focus the discipline gives to sociological and political factors (which are very important to conservation!) had nowhere to go but up, right? Please do correct me if I’ve got the history wrong here, I’m not a conservation biologist…

Another thought: isn’t the story here one of increasing interdisciplinarity of conservation biology, incorporating more of the sociological and political considerations that aren’t traditionally considered part of ecology? That is, the reason why conservation biology appears to be splitting off from ecology “sensu stricto” (at least by the measures considered in the linked paper) is precisely that conservation biology is becoming more interdisciplinary than it used to be? Which if so isn’t quite a story about increasingly-narrow niche specialization and increasingly-separated disciplinary silos, is it?

Just offhand thoughts, which may well reflect lack of background knowledge on my part. Thanks for the thought-provoking post.

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Yes, some fields of conservation biology are becoming more interdisciplinary – and so are some fields of ecology. Many of things that the authors claim separate conservation biology as a ‘more relevant’ discipline, like social/political considerations, are already being addressed by some ecologists and have been inherent to some fields of ecology for years – hence why many ecologists publish in many of the journals they assign as representing cons biol. I think it sounds like they are trying to specifically take aim at theoretical ecology, but don’t specifiy this – sure theoretical ecology may be treated as quite distinct from modern applied conservation biology. But it would be pretty hard to prove that ecological theory has never been useful to conservation success. And there are many conservation biologists that use highly theoretical methods, eg decision theory. Just too many confounding factors to make any of these claims!

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Article contents

Interdisciplinarity: its meaning and consequences.

• Raymond C. Miller Raymond C. Miller Department of International Relations, San Francisco State University
• https://doi.org/10.1093/acrefore/9780190846626.013.92
• Published in print: 01 March 2010
• Published online: 20 November 2017
• This version: 27 August 2020
• Previous version

Interdisciplinarity is an analytically reflective study of the methodological, theoretical, and institutional implications of implementing interdisciplinary approaches to teaching and research. Interdisciplinary approaches in the social sciences began in the 1920s. At a minimum, they involve the application of insights and perspectives from more than one conventional discipline to the understanding of social phenomena. The formal concept of interdisciplinarity entered the literature in the early 1970s. The scholars responsible all shared the thought that the scientific enterprise had become less effective due to disciplinary fragmentation and that a countermovement for the unification of knowledge was the proper response. However, not all interdisciplinarians believe that the unification of existing knowledge is the answer.

There are many ways of differentiating between types of interdisciplinary approaches. One classification distinguishes between multidisciplinary, crossdisciplinary, and transdisciplinary approaches. Multidisciplinary approaches involve the simple act of juxtaposing parts of several conventional disciplines in an effort to get a broader understanding of some common theme or problem. Crossdisciplinary approaches involve real interaction across the conventional disciplines, though the extent of communication; thus, combination, synthesis, or integration of concepts and/or methods vary considerably. Transdisciplinary approaches, meanwhile, involve articulated conceptual frameworks that seek to transcend the more limited world views of the specialized conventional disciplines. Even though many believe that interdisciplinary efforts can create innovative knowledge, the power structure of the disciplinary academy resists interdisciplinary inroads on its authority and resources.

• academic discipline
• area studies
• interdisciplinary approaches
• interdisciplinarity
• interdiscipline
• multidisciplinary
• cross-disciplinary
• transdisciplinary

Updated in this version

Updated references; major revisions throughout.

Introduction

As early as the 1920s, the US Social Science Research Council (SSRC) recognized that, in only several decades after its invention, the departmental/disciplinary structure of the university was becoming an obstacle to effectively addressing comprehensive social problems. Especially in the 1930s, 1940s, and 1950s, the Rockefeller Foundation and then the Ford Foundation worked with the SSRC to fund interdisciplinary research and teaching in US higher education. In the early Cold War era, area studies programs were major recipients of that funding. As a consequence, international studies during this period were often conceptualized as interdisciplinary (Calhoun, 2017 ). At the founding of the International Studies Association (ISA) in 1959 , its mission statement explicitly states that the ISA “promotes interdisciplinary approaches to problems that cannot fruitfully be examined from the confines of a single discipline” ( International Studies Perspectives , May, 2007 , back cover).

The first section of this essay is a historical survey of selected professional literature on interdisciplinary studies, beginning with the classic 1972 OECD Report on its Paris conference (Apostel, 1972 ). It was the first major book entitled Interdisciplinarity . To achieve some conceptual clarity on the many varieties of interdisciplinary activity in the academy, basic terms were defined and a typology proposed. The second major part of this essay is structured by that typology of multidisciplinary, crossdisciplinary, and transdisciplinary approaches. Since all of these categories rely on disciplines as the core ingredient, discipline is also defined.

In recent years, the concept interdisciplinarity has become popular among scholars. Many books and articles have it in their titles. Books on interdisciplinary approaches vary from those promoting interdisciplinarity (Farrell, Lusatia, & Vanden Hove, 2013 ) to those denigrating it and praising the superior qualities of the disciplines (Jacobs, 2014 ). Furthermore, the widespread discussion of interdisciplinarity does not mean that it has politically succeeded in the academy. By and large the conventional disciplines have maintained their power over the university and funding bureaucracies. The last section of this essay discusses the varying fortunes of interdisciplinary approaches in the academy, especially in reference to international relations.

Historical Survey of Select Literature

The noun interdisciplinarity made its professional debut in a 1972 publication from the Organization for Economic Cooperation and Development (OECD). The report, entitled Interdisciplinarity: Problems of Teaching and Research in Universities (Apostel, 1972 ), was sponsored by OECD’s Parisian-based Centre for Educational Research and Innovation. The Report had chapters written by scholars from six different European countries: Austria, Belgium, France, Germany, Switzerland, and the United Kingdom. Though there were many differences between them, they all shared the thought that the scientific enterprise had become less effective due to disciplinary fragmentation, and that a counter movement for the unification of knowledge was the proper response. The problem was “how to unify knowledge and what the many implications of such unity are for teaching and research in the universities …” (Apostel, 1972 , p. 11). Unification “means the integration of concepts and methods in these disciplines” (pp. 11–12). A number of unifying schemas were proposed, including mathematics, linguistic structuralism, Marxism and general systems. Although the authors had different “transdisciplinary” proposals, they all agreed that “interdisciplinarity is a way of life. It is basically a mental outlook which combines curiosity with open mindedness and a spirit of adventure and discovery. . . .” It is practiced collectively. . . . It teaches that there can be no discontinuity between education and research” (Apostel, p. 285).

In addition to a number of important theoretical articles, the OECD report had a major emphasis on the design and implementation of interdisciplinary universities. The authors of that section, Asa Briggs of Sussex University and Guy Michaud of the University of Paris, gave as their sample model an interdisciplinary university with a special emphasis on international relations. They believed that because the field of international relations had the most complex connections, it necessarily involved the study of many methods, disciplines, issues, languages, and geographical areas. All students of their proposed university were expected to be familiar with the basic approaches and concepts of anthropology, politics, economics, international law, ecology, geography, history, sociology, and ethno-psychology (Apostel, 1972 , pp. 253–257).

Chronologically, the next major book that addressed the general issue of interdisciplinarity in the university setting was entitled Interdisciplinarity and Higher Education . It was published in 1979 , and its editor was Joseph Kockelmans, the Director of the Interdisciplinary Humanities Program at Pennsylvania State University. Possibly because he was European-educated, his orientation was similar to the authors of the OECD Report. He argued that only through “philosophical reflection” can the society’s intellectuals approach the “totality of meaning.” To overcome the fragmented worlds that they have created, they need to reach agreement not only on the position of the sciences, but also on “religion, morality, the arts and our sociopolitical praxis” (Kockelmans, 1979 , pp. 153–158). However, Kockelmans was opposed to using a pre-existing framework, such as the ones listed above in the OECD Report, or the logical positivism of the Unification of Science movement spearheaded by the Vienna Circle in the 1930s. None of them fulfilled the comprehensive vision that Kockelmans advocated.

In October of 1984 , OECD, in collaboration with the Swedish National Board of Universities and Colleges, decided to hold a conference to revisit the concept and experience of interdisciplinarity. More than half of the participants were from Sweden, and almost half of them were from one university, Linköping. Linköping University was especially interested in the topic because it had instituted a doctoral program based on four interdisciplinary themes (technology and social change, water in environment and society, health and society, and communication). The proceedings of the conference were published under the title Interdisciplinarity Revisited: Re-Assessing the Concept in the Light of Institutional Experience (Levin & Lind, 1985 ). Essentially the conferees agreed that the early enthusiasm for an interdisciplinary revolution was dampened by the realities of societal and institutional politics. Interdisciplinary research and teaching were still happening, but they were easier to accomplish if the participants did not boldly label them as such. The advisability of keeping a low profile was due to the fact that the “magical slot” from the mid 1960s to the early 1970s, in which interdisciplinary innovation had flourished, was replaced by a more conservative period in which disciplines reasserted their authority. George Papadopoulos of the OECD concluded that, “interdisciplinarity, even when it succeeds in unscrambling existing curricula, remains a hostage to the disciplines” (Levin & Lind, 1985 , p. 208).

The first major work on interdisciplinarity by an American-educated scholar was published in 1990 by Julie Thompson Klein, professor of humanities at Wayne State University. Her book is entitled Interdisciplinarity: History, Theory and Practice . Rather than making an argument for a particular approach, Klein provided a compilation of all the existing literature across all fields of knowledge. She concluded her extensive survey by observing:

Interdisciplinarity has been variously defined in this century: as a methodology, a concept, a process, a way of thinking, a philosophy, and a reflexive ideology. It has been linked with attempts to expose the dangers of fragmentation, to reestablish old connections, to explore emerging relationships, and to create new subjects adequate to handle our practical and conceptual needs. Cutting across all these theories is one recurring idea. Interdisciplinarity is a means of solving problems and answering questions that cannot be satisfactorily addressed using single methods or approaches. Whether the context is a short-range instrumentality or a long-range reconceptualization of epistemology, the concept represents an important attempt to define and establish common ground. (Klein, 1990 , p. 196)

Nowhere in Julie Klein’s extensive bibliography (97 pages long) is there mention of the term international relations or international studies , although she does have a section on area studies.

In 1997 , the Academia Europaea and the European Commission organized a conference in Cambridge, England around the topic “Interdisciplinarity and the Organisation of Knowledge in Europe.” The conference proceedings were published in 1999 under the same title (Cunningham, ed.). There were 24 contributors from 11 countries with most (9) coming from the United Kingdom. Several contributors referred back to the seminal article by Erich Jantsch in the 1972 OECD pioneering publication. Collectively they agreed that modern disciplines were a product of the scientific revolution of the 19th century . The specialized research entities of the University of Berlin seem to have been the origin of the disciplinary structure of knowledge. “Focusing scholarly attention on the essence or nucleus of the individual subject led inevitably to the putting-up of barriers” (Rüegg, 1999 , pp. 34–35). The division into insular, specialized disciplines was seen by sociologists as an almost inevitable outcome of the differentiation associated with the process of industrialization. John Ziman argued that the impetus toward greater and greater specialization had to do with the scholarly requirement for originality. It’s easier to be a “big frog in a small pond” (Ziman, 1999 , pp. 74–75). He concluded his essay by contending that “disciplines stand for stability and uniformity,” whereas “interdisciplinarity is a code word for diversity and adaptability” (pp. 81–82).

In the United States, some of the young scholars in international relations observed the disciplinary narrowing of the field and decided to publish a book in 2000 entitled Beyond Boundaries: Disciplines, Paradigms, and Theoretical Integration in International Studies (Sil & Doherty, 2000 ). A review (Miller, 2001 ) appearing in the newsletter of the Association for Interdisciplinary Studies observed that the book does not deliver on its promise to meaningfully discuss disciplines, paradigms, and theoretical integration; however, it does juxtapose different theoretical positions while calling for international relations scholars to be tolerant and willing to cross boundaries between disciplines and schools of thought.

In 2002 , an English academic, Joe Moran, published a book that he simply entitled Interdisciplinarity . Though broad in comprehension, it focuses on English and cultural studies. He argued that the institutional implications of openly pursuing interdisciplinary approaches are inevitably political, both in the hierarchy of knowledge and in the allocation of material resources (Moran, 2002 ). Oxford University Press decided to enter this academic realm by publishing the Oxford Handbook of Interdisciplinarity (Froderman, Klein, & Mitcham, 2010 ). None of the 37 chapters are primarily on international studies, though one of the chapters uses area studies as an example (Calhoun & Rhoten, 2010 ). In 2017 , the Handbook came out in a second edition (Froderman). Its 46 chapters address many issues, ranging from funding to pedagogy. However, there is still no chapter dedicated to international studies. The philosopher and editor Robert Froderman argued that “interdisciplinarity is the bridge between academic sophists (disciplinarians) and the rest of society” (p. 7).

In 2009 , Pami Aalto of Tampere University in Finland embarked on a major project to discuss and showcase interdisciplinary approaches in international studies. Two books emerged from the project. The first was International Studies: Interdisciplinary Approaches (Aalto, Harle & Moisio, 2011 ), and the second, Global and Regional Problems: Towards an Interdisciplinary Study (Aalto, Harle, & Moisio, 2012 ). Aalto and his fellow editors argue, “We want to assert that International Studies—as a wider field of studies than International Relations—must necessarily be more interdisciplinary than International Relations ever was during its golden era from the 1950s onwards” (Aalto et al., 2011 , p. 3). They observed that, in the inter-war period, international studies was an interdisciplinary field with materials and perspectives drawn from many fields and disciplines. They noted that this sense of the field was spelled out in the 1939 League of Nations publication University Teaching of International Relations (Zimmern) as well as Quincy Wright’s magnum opus The Study of International Relations ( 1955 ). Despite Wright’s extraordinary effort to synthesize over 20 fields into the study of international relations, his influence over the subsequent development of the field has been minimal. International relations, especially in the United States from the 1950s on, has become more and more embedded in political science. A key reason for this evolution was the focus on the cold war power conflict. Ironically, a major intellectual force in this development was Quincy Wright’s colleague at the University of Chicago, Hans Morgenthau. However, with the end of the Cold War era, Aalto and his fellow editors were hoping for the emergence of a broader, more diverse, interdisciplinary approach to international studies (Aalto et al., 2011 , pp. 11–19).

In 2013 , two European-based scholars, Andrew Barry and Georgina Born, published a book in which they claimed to rethink what is meant by interdisciplinarity, entitled Interdisciplinarity: Reconfigurations of the Social and Natural Sciences . For instance, the authors challenge the conventional statement that interdisciplinary activity is about combining and integrating knowledge from existing disciplines. They believe that interdisciplinarity is about gathering knowledge from all available sources, not just disciplines. They point to community-based knowledge, local experience, and indigenous knowledge, among other sources. Also, they start with the premise that neither disciplinary nor interdisciplinary activities are monolithic or unchanging. Disciplines do have the political advantage in the academy because they usually control the curriculum and the budgets that include faculty hiring. Thus, the disciplines have considerable control over the conditions that determine the degree of receptivity to interdisciplinary research and teaching in any particular university setting. In Barry and Born’s opinion, truly interdisciplinary activities have qualities that differentiate them positively from the disciplines. These three qualities are accountability, innovation, and ontology. Accountability means being more responsive to societal needs. Innovation means being more practical about the problems that are addressed. And ontology means that interdisciplinary activities are more likely to be relational, holistic, and to view humans as being embedded in nature. Also, they respect the participation of the public in the discovery and application of knowledge. But interdisciplinary programs come and go. Some have staying power and become established interdisciplines, even new disciplines. Some get absorbed, whereas others disappear altogether. “The chapters in this book attest to the heterogeneity that characterises both disciplines and interdisciplines and the necessity of probing the genealogies of particular interdisciplinary problematics” (Barry & Born, 2013 , p. 41).

The American Political Science Association noted the increasing popularity of interdisciplinary rhetoric and practice, and in 2007 , they established a Task Force to study it. The report of the Task Force was published under the title Interdisciplinarity: Its Role in a Discipline-Based Academy (Aldrich, 2014 ). The report is interesting because of the obvious tension that permeates the document between proponents of disciplinarity and interdisciplinarity. The first chapter reiterates the value of disciplines. The Task Force Chair, John Aldrich, argued that disciplines are the foundation of knowledge and the academy. In his view, interdisciplinary efforts often lack valid and reliable measures for judging scholarship and teaching, and thus are inherently inferior. Nevertheless, in a subsequent chapter, four pioneers of interdisciplinary scholarship argued for the superior merits of interdisciplinary approaches. The four are David Easton (systems), R. Duncan Luce (cognitive science), and Susanne and Lloyd Rudolph (area studies). In fact, Easton stated, “I don’t see anything that can possibly be exciting and not be interdisciplinary. I think the disciplines have sort of exhausted their contributions to our understanding of politics” (Aldrich, 2014 , p. 55). Lloyd Rudolph concluded his interview by offering this reflection: “I realize that it is not only that I value interdisciplinarity but also that I value being allowed to think out of the box of disciplinary methods. New concepts reveal new realities” (Aldrich, 2014 , p. 72).

In 2013 and 2015 , two books were published that had both “interdisciplinary” and “international relations” in their titles. The first was Interdisciplinary Perspectives on International Law and International Relations: The State of the Art , edited by Jeffrey Dunoff and Mark Pollack ( 2013 ). A more accurate title would have been “interdisciplinary perspectives on the historical relationship between international law and international relations.” The authors noted that during the inter-war period, scholars in the two fields worked very closely together. However, with the advent of World War II and the rise of realism as the dominant theory in international relations, the study of law was considered irrelevant, as unenforceable international law does not affect the behavior of nation-states. Furthermore, normative law was considered too non-scientific for the post-World War II behavioralists/positivists political scientists. It’s worth noting that the editors consider international relations a discipline and that they seem to use it interchangeably with political science. With the rise of other theories in international relations that challenged the dominance of realism, international law became a more acceptable ingredient of international relations scholarship in the 1990s and thereafter. However, instead of a more equal relationship between two disciplines, international law was often considered a subject rather than a discipline. Or as the editors put it, “the intellectual terms of trade were asymmetrical” (Dunoff & Pollack, 2013 , p. 649). The interdisciplinary perspective of the editors and their fellow authors is reflected in their call for more pragmatic, eclectic theoretical approaches drawn from both international relations and international law. “Our call therefore is not for token inclusion of international law approaches, but rather for an interdisciplinary version of the pragmatic, analytically eclectic, tool-kit approach” (p. 653).

The second book, edited by Patrick James and Steve Yetiv, was Advancing Interdisciplinary Approaches to International Relations (Yetiv & James, 2015 ). Their advancement illustration is the application of many perspectives from different disciplines and interdisciplines to the topic of conflict studies. These include history, political science, psychology, neuroscience, anthropology, gender studies, technology studies, demography, and systems analysis (p. 324).

In 2016 , the British Academy published a report on its investigation of interdisciplinary research and teaching in higher education in the United Kingdom. It is entitled Crossing Paths: Interdisciplinary Institutions, Careers, Education and Applications . The working group was chaired by David Soskice of the London School of Economics. In his preface, he recognized the need to promote interdisciplinarity. According to him, this was necessary because the universities, the research councils, the journals and publishers were organized along disciplinary lines. “The incentive structures set up by the interplay of these institutions militates against interdisciplinarity” (p. 5). Then, paradoxically, Soskice went on to argue, as did the group report, that the best way to promote interdisciplinarity is the support of “strong disciplines” (Soskice, 2016 , p. 6). This seems like a strategy that would perpetuate the problem they have identified. The group recommended that junior faculty should first make their reputations in a home discipline. Only then would it be safe to venture into interdisciplinary territory (p. 9). However, once socialized in the discipline’s world view, it’s less likely that faculty will venture into interdisciplinary territory.

The British Academy report recognizes that getting a credible and fair evaluation of interdisciplinary research is very difficult in a discipline-controlled environment. Nevertheless, the working group recommended “evaluating the whole and not just disciplinary parts of any interdisciplinary output. The quality of interdisciplinary work lies in the way that it brings disciplines together” (Soskice, 2016 , p. 10). The evaluation chapter provides a set of guidance questions for research-review panels for evaluating interdisciplinary research proposals. One of the questions asks whether the proposal shows “an understanding of the challenges of interdisciplinary integration, including methodological integration, and the human side of fostering interactions and communication.” Therefore, it is not surprising that the chapter ends with the statement, “a focus on interdisciplinarity revives a sense of the academy as a holistic intellectual and social organism, integrated into the wider community, in which multiple flows and exchanges between all of its parts ensure its vitality” (Soskice, 2016 , p. 70).

In 2019 , Issues in Interdisciplinary Studies dedicated an entire issue to the work of the most prolific American scholar of interdisciplinarity, Julie Thompson-Klein (Augsburg, 2019 ). Her newest book is scheduled to be published in 2021 with the title Beyond Interdisciplinarity: Boundary Work, Communication, and Collaboration in the 21st Century . The book focuses on a full range of sector-crossing, including not only academic disciplines, but also occupational professions, interdisciplinary fields, public and private spheres, local communities, project stakeholders, and countries and cultures across the globe, wherever knowledge production is occurring. This new book is an update and extension of her earlier work, Crossing Boundaries: Knowledge, Disciplinarities, and Interdisciplinarities (1996) .

Academic Discipline

Disciplines are the basic units in the structure of knowledge that have been “historically delineated by departmentalization. Within each discipline there are rational, accidental, and arbitrary factors responsible for the peculiar combination of subject matter, techniques of investigation, orienting thought models, principles of analysis, methods of explanation and aesthetic standards” (Miller, 1982 , p. 4). They constitute the bureaucratic subcultures of the modern university. The modern disciplinary system was established at the turn of the 19th into the 20th century .

Many scholars have tried their hand at the task of explicating the characteristics of an academic discipline, but the list provided by Arthur King and John Brownell ( 1966 ) in The Curriculum and the Disciplines of Knowledge still seems among the clearest and most comprehensive. Below is this author’s version of their original list:

Field of demarcated study (subject matter boundaries, inclusions and exclusions).

Shared set of underlying premises (basic assumptions about how the world works).

Shared set of concepts (jargon).

Shared set of organizing theories/models (explanatory frameworks).

Shared set of truth-determining methods (what counts as data—how to make sense of them—i.e. research protocols).

Shared set of values and norms (preferred approaches to the material field that is studied by the discipline—e.g. economists prefer the approach of the free market; also preferred conduct by the practitioners of the discipline).

These six qualities cumulatively come together as a unique perspective—a coherent world view—a disciplinary paradigm or matrix.

Community of scholars who share this world view (professional identity—academic tribes ).

Shared set of literature and great scholars in the discipline.

Agreement on what to teach (structure and content of the basic texts and curriculum from the introductory course to the advanced graduate seminars).

Means of reinforcing the professional standards (graduate training, hiring and tenure control, associations, conferences, peer-reviewed journals, and grant-making processes).

Departmental home in a college/university (bureaucratic recognition, resource allocation and territorial ownership).

Ideal-type conceptualizations of this nature have great heuristic value, but applying them in the “real world” becomes problematic. After all, every group of faculty organized around a defined academic interest that has aspirations for permanence, wish to be known, at least eventually, as a discipline. Recognition as a discipline means more prestige and the prospect of more dependable institutional support. A working solution to this definitional problem is to limit the designation of discipline to those departmental groupings that appeared at the beginning of the 20th century and have institutionally solidified their presence in the academy over the past 100 plus years. John Ziman called them the “Grand Old Disciplines” ( 1999 , p. 73). Thus, in the social sciences, the conventional and building-block disciplines would be Anthropology, Economics, Geography, History, Political Science, Psychology and Sociology. Without some kind of limitation on the use of the designation discipline, even the distinction between discipline and interdisciplinary can become meaningless. Nevertheless, the solution proposed is admittedly an arbitrary one, but the historical process that created these disciplinary conglomerates in the first place was also a relatively arbitrary process. Eric Wolf argued that the field of classical political economy was divided into the specialized disciplines of economics, political science, sociology and anthropology in a process that lost touch with the real world.

Ostensibly engaged in the study of human behavior, the various disciplines parcel out the subject among themselves. Each then proceeds to set up a model, seemingly a means to explain “hard,” observable facts, yet actually an ideologically loaded scheme geared to a narrow definition of subject matter. (Wolf, 1982 , p. 10)

The establishment of these specialized disciplines at the beginning of the 20th century has been called the “academic enclosure” process (Becher, 1989 ). In a few decades, these disciplines had enclosed themselves in departmental organizations that gave them long-term bureaucratic protection. Yet these disciplines, according to Weingart and Stehr, are “the eyes through which modern society sees and forms its images about the world, frames its experience, and learns, thus shaping its own future or reconstituting the past” (Weingart & Stehr, 1999 , p. xi). Stephen Turner argued that “disciplines are shotgun marriages . . . and are kept together by the reality of the market and the value of the protection of the market that has been created by employment requirements and expectations (Turner, 1999 , p. 55). Turner believed that the disciplines’ animosity toward interdisciplinary initiatives was primarily driven by protectionism (p. 50).

The seventh disciplinary characteristic notes that the first six qualities come together in a world view that is unique to each discipline. Comparing world view components is a useful method for both disciplinary and interdisciplinary scholars. The concept has German origins and has been productively utilized in many academic and non-academic venues for 150 years. This author was introduced into the way anthropologists use the world view method by Robert Redfield ( 1956 ). According to Redfield, every culture or sub-culture has a world view, its embedded “mental map.” It provides guidance on the nature of the world, how we know the truth about it, what is right and wrong behavior, and what emotionally matters the most. Cognitive linguist George Lakoff contended that “World views are complex neural circuits fixed in the brain. People can only understand what fits the neural circuitry in their brains. Real facts can be filtered out by world views” (Lakoff, 2017 ). Critical psychologist Michael Mascolo noted “the concept of world view is founded on the epistemological principle that observation of the physical and social world is a mediated rather than a direct process” (Mascolo, 2014 , p. 2086). He reaffirmed Redfield’s point that a complete world view has an ontology, an epistemology, and a normative belief system.

Table 1. Post-World War II Macro Social Sciences: Comparative Attributes

Source : Miller, R. C. ( 2018 ). International political economy: Contrasting world views (2nd ed., p. 17). London, UK: Routledge.

This author has used world view as the comparative method in understanding the different schools of thought in international political economy (Miller, 2018 ). One step in this process was identifying the comparative attributes of the basic contributing disciplines. A summary of that analysis is in Table 1 : Post World War II Macro Social Sciences: Comparative Attributes. Economics, political science, and sociology are compared in six fundamental dimensions: core subject matter, central concepts, explanatory strategies, normative orientation, data collection, and data analysis.

Interdisciplinary Approaches

Interdisciplinary approaches in the social sciences involve, at a minimum, the application of insights and perspectives from more than one conventional discipline to the understanding of social phenomena. Interdisciplinarity , on the other hand, is an analytically reflective study of the methodological, theoretical, and institutional implications of implementing interdisciplinary approaches to teaching and research. Strictly speaking, interdisciplinarians are those who engage in the scholarly field of interdisciplinarity, though there are many faculty and others who participate effectively in interdisciplinary projects without being reflexive about its methods, theories, and institutional arrangements. On the other hand, interdisciplinary participants are more likely to be aware of their underlying world views than disciplinarians.

There are many ways of differentiating between types of interdisciplinary approaches, and in fact, of defining the basic term, interdisciplinary. For instance, the National Academies of Science propose that:

“Interdisciplinary research is a mode of research by teams or individuals that integrates information, data, techniques, tools, perspectives, concepts, and/or theories from two or more disciplines or bodies of specialized knowledge to advance fundamental understanding or to solve problems whose solutions are beyond the scope of a single discipline or area of research practice.” (National Academy of Sciences, 2005 , p. 39)

This definition privileges the process of “integration” as well as identifying “disciplines” as the primary source of the ingredients to be integrated. Lisa Lattuca, in her faculty-interview study Creating Interdisciplinarity ( 2001 ) argued that post-structuralists, like herself and all the humanities professors and most of the social science professors in her study, reject both of these privileging assumptions. They argue that integration presumes harmonious order, whereas reality may be full of oppositions and contradictions, and that using disciplines as the basic raw material legitimizes their monopoly over knowledge. However, all of the natural scientists in her study were comfortable with the type of definition proposed by the National Academies (Lattuca, 2001 , p. 104). The Political Science Task Force Report also accepted it. Nevertheless, interdisciplinary approaches could be broadened to include the processes of juxtaposition, application, synthesis, and transcendence as well as integration.

By utilizing this broader definition of interdisciplinary approaches that includes processes other than integration, the logic of the original OECD typology retains its efficacy. That typology divided interdisciplinary approaches into multidisciplinary, crossdisciplinary, and transdisciplinary. What follows is this author’s version of that typology.

Multidisciplinary Approaches

Multidisciplinary approaches involve the simple act of juxtaposing parts of several conventional disciplines in an effort to get a broader understanding of some common theme or problem. No systematic effort is made to combine or integrate across these disciplines. This is the weakest interdisciplinary approach, and it actually enhances the stature of the participating disciplines because their identities and practices are not threatened. They do not need to change any of their protocols, yet they can claim their openness to interdisciplinary cooperation. Cafeteria-style curricula, team-taught courses, ad hoc research teams, and conference panels could be examples of this approach.

Crossdisciplinary Approaches

Crossdisciplinary approaches involve real interaction across the conventional disciplines, though the extent of communication and thus combination, synthesis or integration of concepts and/or methods varies considerably. Since the variety of crossdisciplinary approaches is so great, this author has created a further six-fold typology. The six sub-categories of crossdisciplinary approaches are: (a) topics of social interest, (b) professional preparation, (c) shared analytical methods, (d) shared concepts, (e) hybrids, and (f) shared life experiences (Miller, 1982 ). Hundreds of crossdisciplinary combinations have been created over the course of the last 100 years. Some of these combinations have been ephemeral, some long lasting, but poorly articulated, and some have developed an institutionalized coherence that rivals the conventional disciplines. The latter in this author’s taxonomy are the interdisciplines . David Long, one of the authors in Aalto’s first book called them “neodisciplines” (Long, 2011 , pp. 52–59).

Transdisciplinary Approaches

Transdisciplinary approaches, according to Jantsch’s classic essay ( 1972 ), involve articulated conceptual frameworks that seek to transcend the more limited world views of the specialized disciplines. These frameworks are holistic in intent. In the 1972 OECD volume, the transdisciplinary approaches mentioned were general systems, structuralism, Marxism, and mathematics. The 21st century transdisciplinary movement in Europe believes that the broader public should be involved in providing, testing, evaluating, and implementing knowledge across all fields. Academic disciplines, therefore, are only a part of the picture.

Social Topics

Important social topics frequently attract members from several disciplines. They start out as multidisciplinary groupings, but over time continuous communication creates a new crossdisciplinary field of study. Examples would include environmental studies, cognitive science, gerontology, labor studies, peace studies, and urban studies. The study of geographical regions, area studies, is an interesting topical example because of its close relationship to international relations.

Professional Preparation

Another organizing principle for crossdisciplinary combinations is relevant knowledge for professional preparation . Examples include business management, diplomatic studies, education, public administration, health services, and policy studies. There are undoubtedly more students, faculty, and practitioners in this professional category than in any of the other categories, but the self-conscious attention to their interdisciplinary nature is very limited. Nevertheless, there are exceptions; for instance, Donald Schön ( 1983 ) in his book The Reflective Practitioner observed that the professions are split between the rational technocratic view of the more theoretical and conventional perspective vs. the more particularistic uncertainty of the actual field situations. He tried to find a middle ground between these extremes by proposing a reflexive approach that combines theory and practice. He argued that professionals should be aware of the frames within which they operate so that they are open to critiquing the one they are using and even shift to another if the situation requires it. Schon’s proposed approach is similar to the interdisciplinary method of comparative world views or multi-perspective analysis (Miller, 1982 ).

Policy studies, a growing field in recent years, manifest this internal tension rather dramatically. In the early 1950s, Harold Lasswell expressed his belief that through a rational and scientific process the best policy options could be identified and implemented toward the betterment of democratic objectives. Some of the analytical methods he advocated, such as benefit/cost analysis, are still being applied today. However, his approach has been criticized as being undemocratic, that is, “scientists know better,” and incredibly unrealistic as the political decision-making process is anything but rational. Studying the “different perspectives that underlie conflict in public policy arenas . . . is more illuminating and ultimately more practical than quixotically tilting at scientific windmills” (Smith & Larimer, 2009 , p. 18).

Shared Analytical Methods

Similar research methods, especially the quantitative ones, are often shared across the disciplines. They provide a basis for bringing methods-oriented faculty members together in more permanent crossdisciplinary associations. These groups have conferences, journals, and even academic programs. Examples of these shared analytical methods include statistics, computer modeling, game theory, and information theory (Miller, 1982 ). However, despite the potential cost savings, conventional disciplinary departments are usually unwilling to replace their own methods courses with the more generic ones from these crossdisciplinary programs.

Shared Concepts

There are some major concepts that appear in many disciplines that have the potential for crossdisciplinary integration. Classic examples of shared concepts include energy, value, flows, role, evolution, development, and cycles (Abbey, 1976 ). George Homans, a sociologist in Harvard’s crossdisciplinary Social Relations Department in the 1960s and 70s used exchange as his main integrating concept. The source of his inspiration was rational exchange theory from the discipline of economics (today it would be called rational choice theory). He made an explicit effort to use benefit/cost exchange as the basis of a theory of human behavior that could integrate across disciplines. Homans argued that although the specifics of exchange relationships may vary across different types of human experience, their overall interactive form may be quite similar (Homans, 1974 ).

The concept of development was dominant in the social sciences in the 1950s and 1960s under the crossdisciplinary umbrella of modernization theory. Modernization theory grew out of the need to achieve some degree of coherent coordination between the different and sometimes contradictory development strategies proposed by the separate social science disciplines. Economists argued that development would occur if sufficient amounts of capital investment are made and markets are developed. Political scientists argued that development requires modern bureaucracies, effective governance, and political participation. Sociologists argued that modern social institutions such as factories, schools, and mass media are key components in any development plan. Anthropologists argued that the residents of poor countries had to change their traditional cultural values into modern ones if development were to occur. Psychologists argued that individual personality development is the key, shifting the orientation from ascription to achievement. Modernization theory tried to bring all of these diverse perspectives together. It was the central organizing theory of the crossdisciplinary field of development studies.

The most widely recognized type of crossdisciplinary approach is undoubtedly the hybrids . Hybrids combine parts of two existing, related disciplines to form interstitial new crossdisciplines that attempt to bridge perceived gaps between disciplines (Miller, 1982 ). Well-known examples include social psychology, political economy, biogeography, and historical sociology. Sometimes the hybrid crossdisciplinary fields generate new theories whose promise is so great that they are borrowed back into their constituent disciplines. Social psychology’s symbolic interaction theory is a case in point. In fact, Dogan and Pahre ( 1990 ) argue that hybrid activity is the most likely source of innovative advances.

One of the most important hybrids in the interdisciplinary realm of international relations is political economy, especially in the form of international political economy (IPE). IPE uses the multi-perspective approach mentioned above. It juxtaposes the competing explanatory perspectives of the market model from economics, institutionalism from political science and sociology, and historical materialism from classical Marxist political economy (Miller, 2018 ). The differing perspectives provide a rich treasury of insights, understandings, critiques, and research strategies.

Shared Life Experiences

The basic premise in crossdisciplinary programs based on shared life experiences is that certain groups have shared a common experience of oppression that gives them a shared identity, a shared rejection of mainstream knowledge that reinforces this oppression, and a shared political agenda to replace the unjust social conditions with an egalitarian society. Three major examples of this category are women’s studies, ethnic studies, and post-colonial studies. These crossdisciplinary fields entered the academy as outgrowths of the social movements of the late 1960s and early 1970s. They started out as multidisciplinary challengers to the disciplinary/departmental power structure of the university, yet over the past four decades women’s studies and ethnic studies have evolved increasingly into discipline-like programs, in other words, interdisciplines. According to some observers, one of the costs of this institutional acceptance was the loss of one of the early objectives of these movements, social change activism in the community (Messer-Davidow, 2002 ).

Virtually all of the over 700 women’s studies programs in the United States teach feminist theory, an integrating perspective that focuses on socially constructed gender systems and standpoint analysis. Standpoint theory contends that how one perceives any human condition depends on the position that one occupies in the society. Those who are being oppressed are going to see things very differently than those who are doing the oppressing.

According to Ann Tickner, feminism challenges the neo-positivist and state-centric orientation of international relations in the United States. The unequal relationships that pervade the world are socially constructed and vary from place to place, with women suffering universally from male-dominated exercises of power. Furthermore, dichotomies such as those that “separate the mind (rationality) from the body (nature) diminish the legitimacy of women as ‘knowers’” (Tickner, 2014 , p. 86). Knowledge should not be pursued for its own sake or for the benefit of the state but in order to facilitate the emancipation of the oppressed (Tickner, 2014 , pp. 176–77).

Theorists in African-American or Africana studies have made a deliberate effort to incorporate the perspective of women in their key concept, Afrocentricity . The meaning of Afrocentricity is somewhat contested within the interdiscipline, but there is no doubt about what it opposes, namely Eurocentrism. Among the specified features of Eurocentrism are reductionism, individualism, and domination over nature, whereas Afrocentricity is associated with holism, community, and harmony with nature (Azibo, 2001 , p. 424). Karanja Keita Carroll ( 2008 ) contended that the “Afrikan worldview” has embedded within it an African culture-specific axiology, epistemology, logic, cosmology, ontology, teleology, and ideology that necessitate a research methodology that is consistent with these components. Instead of the Eurocentric approach that emphasizes objective detachment, separation between the knower and the known, material reality as primary, either/or logic, and knowledge for knowledge’s sake, the Afrikan worldview emphasizes full engagement, the blending of knower and known, the spiritual essence of reality as primary, both/and logic, and knowledge for the betterment of African peoples. Africana research is about participation, relationships, interdependence, and the liberation of Africana people (Carroll, 2008 , pp. 4–27).

Advocates for transdisciplinary approaches often directly challenge the efficacy of conventional disciplines, claiming that they are part of the problem rather than the solution, especially when the objective is the mitigation of complex social problems. Proponents of transdisciplinary approaches frequently accuse the hegemonic conventional disciplines of protecting the status quo rather than promoting progressive change. The framers of some transdisciplinary approaches see them as providing alternatives to the world views of the conventional disciplines that they would replace. Examples of discipline-replacement transdisciplinary approaches would be general systems theory, Marxism, cultural studies and sustainability studies. Examples of transdisciplinary approaches that could supplement rather than replace conventional disciplines would be symbolic interactionism, rational choice theory, and gender theory (Miller, 1982 ).

General systems theory, the transdisciplinary approach that Jantsch favored, contends that nature is a hierarchy of similar structures up through the whole succession of physical, biological, and social systems. There are similar developmental patterns throughout nature, but there are different paths that can lead to the same destination. Through the organization of energy from the environment (negative entropy) and communication with the environment (negative feedback), systems seek to maintain dynamic equilibria. This theory conceives of nature as a holistic set of relationships that thrives on diversity.

David Easton introduced systems thinking to political science in the 1950s and 1960s because he felt the discipline was too narrow. “I am not a political scientist but rather a social scientist interested in political problems” (Aldrich, 2014 , pp. 52–53). Currently, Carolyn and Patrick James continue Easton’s systems approach with their application of “systemism” to foreign policy analysis. However, in their view, systemism moves away from Easton’s bias toward homeostatic proclivities and emphasis on the macro level. Systemism includes both the macro and the micro and all forms of interaction between them (James & James, 2015 ).

Since the 1960s, general systems theory has been the main transdisciplinary approach of environmental or ecological studies (Costanza, 1990 ). Today, this field is most likely to be called sustainability studies. In a major conference on transdisciplinarity held in Switzerland in 2000 , sustainability was put forward not only as the major reason for the necessity of transdisciplinarity, but also as a transdisciplinary approach in itself (Klein et al., 2001 ). However, Egon Becker argues that sustainability studies is a “transdisciplinary field” that is more of a “conceptual and heuristic framework” than a general theory ( 1999 , pp. 284–285).

The lack of an agreed-upon general theory for engaging in the intellectual process of integrating across disciplines led William Newell to search for the most comprehensive and functionally effective transdisciplinary theory. He decided on general systems. But the first difficulty that Newell faced was deciding on which version of general systems theory to embrace. He identified eight possibilities: chaos, complex systems, fractal geometry, nonlinear dynamics, second-order cybernetics, self-organizing criticality, neo-evolutionary biology, and quantum mechanics (Newell, 2001 ). After studying them all, he chose complex systems as the preferred approach. Newell ( 2001 , p. 7) explains: “Specifically, the theory of interdisciplinarity studies that I am advocating focuses on the form of complexity that is a feature of the structure as well as the behavior of a complex system, on complexity generated by nonlinear relationships among a large number of components, and on the influence of the components and relationships of the system on its overall pattern of behavior.” Newell presented his preferred theory to a panel of well-known interdisciplinarians for their reactions. None of the six respondents agreed with his suggestion, primarily because they did not believe that the range and diversity of interdisciplinary possibilities could be captured within one theoretical framework (Issues in Integrative Studies 19, 2001 , pp, 1–148)

One of the respondents to Newell’s proposal, Richard Carp ( 2001 ), took issue with his basic premise, namely that the knowledge to be integrated via complex systems theory comes exclusively from existing disciplines. Carp insisted on widening the knowledge sources. He stated that we should stop thinking of “the disciplines as unique sources or resources for knowledge and thought” (Carp, 2001 , p. 74). Carp argued that we should “learn from multiple knowledge formations” (p. 75). Disciplines should not be the “gatekeepers.” The universities are just one of the many institutions in society that not only possess knowledge but can also create it. We should not be talking about interdisciplinary studies but “knowledge formations” (p. 75).

In Europe, the transdisciplinary movement has taken several different directions. The Swiss Academies of Arts and Sciences conference in 2000 promoted a process form of transdisciplinarity that transcended not only disciplinary boundaries, but also the boundary between the scientific establishment on the one hand and the users of the results of scientific research on the other hand. Users include government agencies, businesses, non-profit organizations, and members of the general public. Since all of these groups are stakeholders in the solution of the societal problems that science has an obligation to address, they should all be present at the table in the research process. In fact, the more stakeholders involved, the more “robust” the research. “We take the contributions to the informing and the rationalizing of actions in their societal context to be the main performance of problem-oriented research, and by implication, also of transdisciplinary research” (Zierhofer & Burger, 2007 , p. 57). In other words, according to the Swiss school, the purpose of transdisciplinary research is to seek and facilitate the implementation of solutions for societal problems, such as violence, poverty, and global warming, that serve the common good (Pohl & Hadorn, 2008 ). Norwegian professor Willy Ostreng, in his major book on interdisciplinary research, agrees and adds that as transdisciplinarity traverses the boundaries between science and stakeholder expertise it creates a new science, a “post-normal” science (Ostreng, 2010 , pp. 29–33).

Another European school of transdisciplinarity is centered around Basarab Nicolescu, a French academic. His group is organized around the International Center for Transdisciplinary Research. The movement’s objective is the achievement of the totality of meaning across all the sciences, art, religion, and cultural perspectives. That endeavor involves the search for relations and isomorphisms across all realms. The French school’s epistemology is explicitly non-Aristotelian in that it wishes to go beyond lineal and binary logic. They recognize different levels of reality in which different modes of understanding prevail. They start with the differences between classical physics and quantum physics, between reason and intuition, between information and consciousness, and between linear and non-linear logics. Non-linear logic is explained as the unity of oppositions, the inclusion of the excluded middle, and the evolutionary process of ever more comprehensive syntheses. Manfred Max-Neef calls this epistemology “strong transdisciplinarity.” He sees some of it in the natural sciences, especially in quantum physics and complexity theories. However, he does not see any of it in the social sciences. He sees economics as the most retrogressive and therefore one of the biggest obstacles to a unified, spiritually evolved, sustainable future (Max-Neef, 2005 , pp. 5–16).

There are some interesting analogies between “strong transdisciplinarity” and the field of cultural studies, for which many claim transdisciplinary status. Both approaches are strongly critical of the excessive reliance on rationality and analytic reductionism, as well as of the fragmented specialization of the structure of knowledge. The location of cultural studies at the interface of the humanities and the social sciences enables its practitioners to bring together their different concepts of culture and then to add the additional dimension of everyday meanings and practices present among the broader population (Moran, 2002 ).

It is generally agreed that the institutional origin of cultural studies was at Birmingham University in 1964 . The founders had an anti-establishment orientation informed by Italian neo-Marxist Antonio Gramsci and French post-structuralist Michel Foucault. The Birmingham group wished to understand and challenge the power over the general population that the cultural elites exercised through the mass media and the power that the intellectual elites exercised through their control of the structure of knowledge, that is, the departmental/disciplinary structure of the academy. When cultural studies diffused to the United States, the field lost some of its political agenda; however, it retained its emphasis on popular culture. Numerous academic fields are identified as contributing to cultural studies, including cultural anthropology, textual criticism, art and social history, linguistics, sociology, aural and visual culture, philosophy of science, political economy, communication studies, psychology, and feminism. These multiple sources led Joe Moran ( 2002 , p. 50) to comment, “Cultural studies could be said to be synonymous with interdisciplinarity itself.” It is both ironic and instructive then that the founding enclave of cultural studies, the Birmingham Centre, was shut down by the higher education authorities of the United Kingdom in 2002 , presumably because of the “low quality of its research production” (Klein, 2005 , pp. 52–53).

Consequences

Advocating explicitly for interdisciplinary approaches in a discipline-controlled environment can be risky. It can be politically risky for administrative units and personally risky for faculty, especially for junior faculty. Interdisciplinary approaches do have implications for the structure and politics of knowledge. They have implications for International Relations, especially if the study of international relations is considered an interdisciplinary field. A 2002 publication assessing the field came to this conclusion:

While there seems to be little problem in designating international relations as a “field,” the symposium left unclear whether this field is most properly a subfield of political science, a subfield of several disciplines, an amalgam of the subfields of multiple disciplines or an academic discipline in its own right. (Puchala, 2002 , pp. xvi–xvii)

The dominant location for International Relations in the United States is as a subfield of Political Science (Aldrich, 2014 , p. 5). In the United Kingdom, however, the field of International Relations is more often treated as a separate discipline (Waever, 1998 ). How the field is conceptualized and institutionalized does have implications for its intellectual strategies, the identities of its practitioners, and its access to resources, both on and off-campus. David Long has argued that “it matters whether IR is considered a discipline in its own right or not. It matters in teaching and research not only by what is cut off, but what is encouraged” (Long, 2011 , pp. 59–60). Rudra Sil warned that “inflexible disciplinary structures may very well come to constitute a hindrance to whatever ‘progress’ is possible in our collective efforts to understand aspects of international life” (Sil & Doherty, 2000 , p. 6). Nevertheless, American political scientists are firmly committed to keeping international relations within their fold. A 2002 doctoral dissertation tells the tale of how, in 1986 , the Political Science Department at the University of Pennsylvania (Penn) successfully absorbed the multidisciplinary graduate program in International Relations. It is an interesting tale of money and powerful personalities, and it would probably be more accurately described as a hostile takeover (Plantan, 2002 ).

Even though the author of the dissertation, Frank Plantan, used the language of interdisciplinarity, he did not employ the conceptual distinctions presented above. That is partly because the graduate program of International Relations at Penn was just a multidisciplinary collection of volunteer faculty members from 10 different departments with no separate, dedicated financial support. By centering his analysis on the Penn case study, Plantan limited the operational meaning of interdisciplinary to this loose arrangement of multidisciplinary specialists, an unstable and vulnerable setup. Yet in his discussion of the intellectual development of the field he mentioned several integrating strategies that have crossdisciplinary and even transdisciplinary qualities. His examples included realism, functionalism, behavioralism, neoliberal institutionalism, rational choice, and constructivism. However, in his historical analysis Plantan saw these theoretical perspectives as ideas to fight over rather than as integrating strategies. In his experience, the competitive departmental environment triumphed over interdisciplinary cooperation. Plantan ( 2002 , pp. 374–375) concluded, “The hefty sunk costs of an existing tenured faculty and staff, and a historic mission (however dubious) in the colleges or university’s broader curriculum, accords them a staying power, an inertia, that no interdisciplinary program can hope to achieve whatever its intellectual merit.”

When Robert Axelrod, the President of the American Political Science Association, established a Task Force in 2007 on Interdisciplinarity, he argued that interdisciplinary research is borrowing across disciplinary boundaries, both importing and exporting, but especially exporting (Axelrod, 2008 ). The Task Force Report (Aldrich, 2014 ) argued that interdisciplinary work begins with faculty who are prepared with accumulated deep knowledge in a discipline. To insure that interdisciplinary teaching and research do not endanger the institutional power of the conventional disciplines, the Report placed a major emphasis on discipline-based peer review. They contended that peer review is the preeminent means by which “the value of scientific knowledge can be established,” and peer review is only credible if it comes from an established discipline (Aldrich, 2014 , pp. 13–23). They continued, “Disciplinarity has not yet been successfully transcended as a means to address key values of scholarship—particularly to resolve contested claims about knowledge, to anchor peer review and the authority it carries with it to protect academic freedom, or to manage the labor market” (p. 23).

Interdisciplinarians would find this reasoning self-serving at the very least. After all, one of the main reasons for engaging in truly innovative interdisciplinary activity is to break free of the narrow, restrictive and presumably inadequate contexts of the established disciplines. The National Academies Report ( 2005 ) argues that there are four “drivers” for interdisciplinary research: inherent complexity of nature and society, need to explore areas that are not confined to a single discipline, need to solve societal problems, and the power of new technologies (p. 40). This Report gives several examples, but the most comprehensive is the case of climate change. Research on this complex and vital issue involves 10,000 scientists in 80 countries from more than 20 disciplines, including agricultural scientists, archeologists, atmospheric chemists, biologists, climatologists, ecologists, economists, environmental historians, geographers, geologists, hydrologists, mathematicians, meteorologists, plant physiologists, political scientists, oceanographers, remote sensing scientists, and sociologists (p. 31).

The established disciplines have been attacked by the post-structuralists for being Eurocentric, sexist, racist, pseudo-objective, status quo-protective and structured in a way that is disconnected from reality. To this group of critics both the ontologies and epistemologies of the conventional structure of knowledge are unacceptable (Moran, 2002 ). Paradoxically, some of the academics who espouse these views have managed to find an institutionalized niche in the university in departments or centers of cultural studies, ethnic studies, post-colonial studies, and women’s studies. However, in the process of institutionalization, they seem to have followed the advice of the Political Science Task Force Report: if interdisciplinary projects want to be successful—that is, achieve bureaucratic recognition with regular budgets and assigned faculty positions—you need to behave like an established discipline (Messer-Davidow, 2002 ). Besides those interdisciplines that have successfully entered the university structure since the 1960s, there were many generic interdisciplinary programs that also evolved into departments even though they were founded as challengers to the disciplinary/departmental system. Evidently, the generic-interdisciplinary departments were perceived by the established departments as the most threatening as well as the most vulnerable. As a consequence, whenever conventional departments found sympathetic administrators they embarked on a campaign for their abolition. In the Politics of Interdisciplinary Studies the stories of several of these program eliminations are told. They include programs at Wayne State, Miami of Ohio, Appalachian State, and San Francisco State, among others. (Augsburg & Henry, 2009 ).

The Political Science Task Force Report also describes how the discipline-based peer-review process works in the federal grant-making process, the largest source of extramural funding in the United States. The National Science Foundation (NSF) is probably organized the most pervasively around the conventional or established disciplines. Therefore, disciplinary criteria are used to evaluate most grant proposals submitted to the NSF. There are small programs within NSF that seem to facilitate interdisciplinary projects: The Measurement, Methodology and Statistics Program and the Human and Social Dynamics Program.

Although the National Endowment for the Humanities (NEH) is organized functionally, its reviewing process also relies largely on disciplinary faculty and their criteria for quality. Federal funding agencies reflect and respect disciplinary boundaries, though they do seek ways to attack new problems through interdisciplinary efforts (Aldrich, 2014 , pp. 101–111). However, the ostensibly integrative interdisciplinary projects they fund frequently end up as merely multidisciplinary.

A group that studied the grant-making experience of the Academy of Finland from 1997–2004 discovered, to their surprise, that almost half of the grants (42%) had some degree of interdisciplinarity despite the disciplinary orientations of the review boards. The solution of the study authors to the disciplinary/interdisciplinary divide is to consider all research interdisciplinary. They reason that since disciplinary boundaries are so amorphous and so frequently permeated that maintaining these distinctions is artificial and inhibitive of creativity in research (Bruun, Hukkinen, Huutoniemi, & Klein, 2005 , p. 169). However, ignoring disciplinary boundaries and their associated departmental bureaucracy seems not only unrealistic about the confining power of the disciplinary structure of knowledge, but also politically naive as well.

A further interesting dimension of the International Studies Association (ISA) is the relationship between its many crossdisciplinary sections and the dominant Political Science discipline. Of the 29 sections ( 2019 ), 22 seem crossdisciplinary in nature. Examples include interdisciplinary studies, human rights studies, environmental studies, peace studies, feminist theory and gender studies, and global development studies. For years the leadership of the ISA seemed merely to presume, despite the organization’s claim to interdisciplinarity, that all the section program chairs could gather at the annual Political Science Convention to review the draft program of the upcoming ISA Convention. The implicit assumption in this past ISA practice was that the section program chairs were most likely political scientists who would be attending the annual Political Science Convention. This assumption always struck this author as problematic, especially in light of the organization’s mission statement and its interdisciplinary membership. The greater efficiency of the Internet facilitated the discontinuance of this practice.

The history of the relationship of area studies to International Relations is a fascinating one in itself. The ISA section sponsoring this contribution, the Interdisciplinary Studies Section, was originally established by area studies scholars according to Fred Riggs, one of its founders. In the 1970s, area studies scholars were contemplating founding a separate umbrella organization for all area studies programs, but they were persuaded to stay within the ISA as an independent section. Area studies centers were established in elite universities after WWII as part of a national Cold War strategy. They were “among the most far-reaching interdisciplinary projects in American higher education” (Aldrich, 2014 , p. 89). Their responsibility was to provide information on the geographic regions of the world in support of the national interests of the United States. Participating faculty came mostly from language, literature, anthropology, history, and political science (international relations) departments. The centers, despite their holistic aspirations, were multidisciplinary in form and particularistic in methodology. Money and guidance ostensibly came from private sources, such as the Ford Foundation and the Social Science Research Council (SSRC), but they actually came from the Department of Defense and the Central Intelligence Agency (Cumings, 2002 ).

In the first few decades after World War II, the study of international relations was significantly oriented to area studies because the money flowing into the universities supported area studies type of knowledge. The legacy of that emphasis is reflected in a 2006 Teagle Foundation survey that found in the responses of 109 Liberal Arts Colleges, half of the top ten interdisciplinary majors were in area and international studies. Since the end of the Cold War between the United States and the Soviet Union, extramural teaching and research support has dwindled significantly for area and international studies. Lloyd Rudolph comments, “after the close of the Cold War, the disciplines and the ‘methodists’ succeeded in attacking and defeating the area studies orientation of Ford and via Ford the SSRC” (Aldrich, 2014 , p. 70). Area studies programs have had to endure criticism from those who see them as a “colonial enterprise” (faculty in post-colonial and ethnic studies programs), while many in the disciplines see them as lacking any theoretical coherence and methodological rigor. From the perspective of conventional disciplinarians, their region-centric particularism and their multidisciplinary structures make them the poster examples of what ails interdisciplinary programs (Miyoshi & Harootunian, 2002 ; Szanton, 2004 ).

Nevertheless, despite the continuing identity crises in area studies, they have managed to survive. Their latest restoration positions them as part of the internationalization of the academy, presumably made necessary by the knowledge demands of globalization and regional hot spots such as the Middle East. However, the continuing viability of area studies remains uncertain. As one observer noted, the different area studies faculties are as separated from each other as the members of disciplines are from each other. “By and large, the world area studies tribes inhabit relatively watertight intellectual domains” (Lambert, 1991 , p. 184). This observation is consistent with the author’s experience. As an administrator in charge of curriculum development, he suggested that the area studies programs could share a core course in which the common methodological principles of area studies could be explored. The area studies faculty, however, were not interested. Nevertheless, David Szanton hopes that participation in area studies programs have helped to “deparochialize” disciplinary faculty, though it does not seem to have lowered the heights of the disciplinary walls. Maybe by being one of the first interdisciplinary programs to use identity as one of its key concepts, area studies may have prepared the way for ethnic studies, women’s studies and post-colonial studies (Szanton, 2004 ).

The case of international political economy (IPE) also raises a number of interesting interdisciplinary issues. In its reincarnation over the last four decades or so, it fits in the category of crossdisciplinary hybrids. IPE’s location in the structure of knowledge is as confused as International Relations. The disciplines of Economics, Political Science and Sociology all claim IPE as a subfield. However, Marxists, in the tradition of classical political economy, see political economy as an overarching, holistic frame in which cultural, economic, political, and social dimensions are inter-related subsets. According to Marxists, the establishment of the specialized disciplines around these dimensions is a part of the hegemonic strategy of capitalism to obfuscate the oppressive nature of the capitalist system.

The late British political economist Susan Strange, a non-Marxist, complained about the lack of knowledge sharing across disciplinary boundaries. She was especially critical of the way in which economists and political scientists ignored each other and their respective knowledge domains. She accused American scholars of International Relations of being too narrowly connected to state-centric political models that did not include serious economic analysis. In fact, she argued, “Far from being a subdiscipline of international relations, IPE should claim that international relations are a subdiscipline of IPE” (see Strange, in Lawton, Rosenau, & Verdun, 2000 , p. 412). Susan Strange is among the “Magnificent Seven” that Benjamin Cohen singled out in his intellectual history of international political economy (Cohen, 2008 , p. 8). She was the leader of the “British School,” which is more holistic, interdisciplinary, and explicitly normative in contrast to the “American School,” which is more positivistic in orientation. Cohen continued his geographic schools of thought analysis of IPE in a 2014 publication, Advanced Introduction to International Political Economy . In response to criticism of the limitations of his original dichotomy, he added schools of thought based in continental Europe, Latin America, and China. He also recognized “leftist” or “heterodox” schools in the United States and the British Commonwealth. However, his geographic schools of thought approach focused primarily on national/regional and cultural differences, rather than theoretical.

Members of all schools of international political economy would probably be comfortable having their field identified as an “interdiscipline” (Underhill, 2000 ). An interdiscipline is a crossdisciplinary field that approximates the characteristics of an academic discipline, but it does not qualify as a 20th century conventional discipline. In fact, maybe International Relations would also best be characterized as an “interdiscipline.” However, that identification still leaves unanswered where International Relations fits in the power hierarchy of knowledge.

According to Barry Buzan and Richard Little, members of the English or British School of International Relations, the widespread placement of International Relations in the United States as a subfield of Political Science has significantly limited its theoretical potential. Buzan and Little ( 2001 ) argued that American International Relations is dominated by an ahistorical, Eurocentric, Westphalian, political/military model. One of the consequences of this approach is the preference for “fragmentation into the anarchy of self-governing and paradigm-warring islands of theory rather than integration into the imperial or federative archipelago of theoretically pluralist grand theory” (Buzan & Little, 2001 , p. 31). Margaret Hermann, in her 1998 ISA presidential address, expressed seemingly similar sentiments about fragmentation: “The field has become an administrative holding company rather than an intellectually coherent area of inquiry or a community of scholars” (Hermann, 2002 , p. 16). However, her solution is a respectful dialogue that builds a “mosaic of multiple perspectives” around problems that are issues of “world politics” (pp. 31–33). She does not seem to be recommending “grand theory” nor going beyond Political Science. Thus, hers is an intra-disciplinary rather than an inter-disciplinary solution. On the other hand, Hermann does seem to embrace the “interdisciplinary mental outlook” advocated by the authors of the pioneering OECD Report (Apostel, 1972 ).

Understanding the different types of interdisciplinary approaches and their differentiation from disciplinary approaches gives one deeper insight into the knowledge production and transmission process. If International Relations is to be a truly independent, interdisciplinary field that can take full advantage of multiple perspectives and methodologies in order to deal more effectively with global problems, it needs to liberate itself from the embrace of confining disciplines, especially Political Science.

Acknowledgments

The author wishes to thank the following for helping to improve this article: Stanley Bailis, Felicia Krishna-Hensel, Renee Marlin-Bennett, Tina Mavrikos-Adamou, Anja K. Miller, and Julie Thompson-Klein.

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Learn About Interdisciplinary Research

Research approaches.

The U.S. National Science Foundation gives high priority to research that is interdisciplinary — transcending the scope of a single discipline or program.

NSF's support of interdisciplinary research and education is essential for accelerating scientific discovery and preparing a workforce that addresses scientific challenges in innovative ways.

This page covers the ways NSF supports interdisciplinary research and how to prepare an interdisciplinary proposal, including how to submit an unsolicited proposal when there is no natural " home " for it in one of NSF’s existing programs.

On this page

What is interdisciplinary research.

Credit: Jurgen Schulze, University of California, San Diego

The definition of a "discipline" and the varieties of cross-disciplinary research — from multidisciplinary, to interdisciplinary, to transdisciplinary — are constantly evolving. Although there is not always agreement on these definitions, it is clear that areas of research are dynamic: continually emerging, melding and transforming. What is considered interdisciplinary today might be considered disciplinary tomorrow.

A working definition of interdisciplinary research can be found in the U.S. National Academies of Sciences, Engineering and Medicine's report, Facilitating Interdisciplinary Research :

Interdisciplinary research:

• Integrates information, data, techniques, tools, perspectives, concepts or theories from two or more disciplines or bodies of specialized knowledge.
• Can be done by teams or by individuals.
• Advances fundamental understanding or solves problems whose solutions are beyond the scope of a single discipline or area of research practice.

How does NSF support interdisciplinary research?

1. Solicited interdisciplinary research

Numerous NSF programs are designed to be explicitly interdisciplinary. Solicitations, which invite proposals to these programs, are posted on the NSF website . NSF's interdisciplinary research programs broadly fall under the three categories below:

Cross-cutting programs

Many of NSF's interdisciplinary programs involve several of NSF's directorates. Examples of these programs include:

• Building Theoretical Foundations for Data Sciences (TRIPODS)
• Coastlines and People
• Dynamics of Integrated Socio-Environmental Systems
• Ecology and Evolution of Infectious Diseases  
• Growing Convergence Research
• Research on Emerging Technologies for Teaching and Learning
• Smart and Connected Communities

Areas of national importance

NSF develops funding portfolios that focus on complex societal challenges of national interest, often in collaboration with other federal agencies. Examples of these programs include:

• The Future of Work at the Human-Technology Frontier
• National Artificial Intelligence Research Institutes
• Navigating the New Arctic
• Understanding the Rules of Life

Center competitions

Many of the centers funded by NSF bring together interdisciplinary research teams. Examples of NSF's center competitions include:

• Materials Research Science and Engineering Centers
• Science and Technology Centers

2. Unsolicited interdisciplinary research

NSF invites interdisciplinary proposals that are not targeted by a program solicitation, as long as they are appropriate for NSF support . Depending on its focus, such a proposal may:

• Be reviewed by a single core program.
• Be co-reviewed by more than one program.
• Extend beyond the scope of any current program.

See " How to prepare an interdisciplinary proposal " to learn how to submit an unsolicited interdisciplinary research proposal.

3. Education and training

NSF has numerous programs supporting the development of the next generation of researchers. The support from these programs is in addition to the support for undergraduates, graduate students and postdoctoral researchers to conduct research on NSF-funded grants. Examples of these programs include:

• Research Traineeship Program
• Research Experiences for Undergraduates

4. Workshops, conferences and symposiums

NSF sponsors forums designed to promote interdisciplinary perspectives and research.

How to prepare an interdisciplinary proposal

Preparing an unsolicited interdisciplinary proposal.

Follow the guidance below for how to submit a proposal with ideas that are in novel or emerging areas extending beyond any particular NSF program.

1. Prepare a summary of your proposal ideas.

Develop a short 1–2 paragraph description of your proposal idea that you can send by email and discuss with NSF staff. Make sure your idea is appropriate for NSF funding by viewing the Programs and Funding Opportunities section of the agency's Proposal and Award Policies and Procedures Guide .

2. Contact an NSF program officer.

The program officer you contact will provide guidance on how and where to submit your proposal. To find an appropriate program officer, consider these options in the following order:

• Identify a program officer through an existing NSF program. In many cases, there will be an existing NSF program for which the proposal idea may be appropriate. Read the program description or solicitation. If your idea seems appropriate, contact one of the program’s program officers.
• Identify a program officer through other means. If your proposal doesn’t clearly fit an existing program, it may make sense to first contact a program officer with expertise in your discipline. They may consult with other NSF staff or recommend another officer for you to contact. You may also contact a program officer you already know, such as one who is managing an award for you or who you met at a conference. 
• Contact a point of contact listed below. If you think your proposal will be of particular interest to one NSF directorate or office, reach out to the relevant point of contact for that directorate. That person is responsible for identifying a program officer in that directorate who will discuss your proposal with you.

Who to contact

The contacts below are responsible for identifying a program officer in their directorate who will discuss your proposal with you.

If there is not an obvious point of contact from one of the options below, email NSF at [email protected] or call (703) 292-4840.

Cross-directorate, NSF-wide

Jessica Robin, OD/OISE

Telephone: (703) 292-8706

Email: [email protected]

Office of Integrative Activities

Randy Phelps, OD/OIA

Telephone: (703) 292-5049

Email: [email protected]

Directorate for Biological Sciences

James O. Deshler, BIO/DBI

Telephone: (703) 292-7871

Email: [email protected]

Directorate for Computer and Information Science and Engineering

James Donlon, CISE/CCF

Telephone: (703) 292-8074

Email: [email protected]

Directorate for Education and Human Resources

Gregg E. Solomon, EHR/DRL

Telephone: (703) 292-8333

Email: [email protected]

Directorate for Engineering

Sohi Rastegar, ENG/OAD

Telephone: (703) 292-5379

Email: [email protected]

Directorate for Geosciences

Barbara Ransom , GEO/OAD

Telephone: (703) 292-7792

Email: [email protected]

Directorate for Mathematical and Physical Sciences

Dean Evasius, MPS/OAD

Telephone: (703) 292-7352

Email: [email protected]

Directorate for Social, Behavioral and Economic Sciences

Brian Humes, SBE/SES

Telephone: (703) 292-7281

Email: [email protected]

Preparing a proposal for an existing program?

If you are submitting a proposal to an existing program that is designed to be interdisciplinary or encourages interdisciplinary work, simply prepare your proposal in accordance with the program description or solicitation.

Frequently asked questions (FAQ)

1. does an interdisciplinary proposal have to be transformative.

No. The extent to which a proposed project is potentially transformative is just one of the considerations included in NSF's Intellectual Merit review criterion. See NSF's " Proposal and Award Policies and Procedures Guide " for more details.

2. Will interdisciplinary proposals be given preference when funding recommendations are made?

If a proposal is reviewed through an existing NSF program, this will depend on the program's criteria.

Some programs are specifically restricted to interdisciplinary research topics. In those programs, a great deal of weight is given to "interdisciplinary" aspects of the proposed work. Some other NSF programs, while not so restricted, explicitly encourage interdisciplinary research and consider it as a positive factor.

In programs that do not distinguish interdisciplinary research as a priority, the review will be based on the combined assessment of the project according to NSF's Merit Review criteria and any other special criteria that may be part of the program's solicitation or description. In these programs, interdisciplinary proposals that advance the program goals are encouraged and funded, and any "weight" is based on the anticipated potential of the project, not whether it is interdisciplinary or single-disciplinary in nature.

Finally, if a proposal is not reviewed through an existing program, it will be reviewed using the two NSF Merit Review criteria: Intellectual Merit and Broader Impacts.

3. Has NSF set aside funds for interdisciplinary research proposals?

Collaborations of interdisciplinary teams are encouraged throughout many NSF solicitations. For example, facility and center programs may call for interdisciplinary efforts.

In programs that do not explicitly call for interdisciplinary research, funds are not set aside for such proposals. However, a division, office or directorate may designate funds to support projects with noteworthy characteristics or potential, which could result from an interdisciplinary approach.

4. I discussed my ideas for an interdisciplinary proposal with several program officers but was discouraged to submit. What are my options?

Program officers play a critical role in providing guidance to the community on the various funding opportunities at NSF. You may have been discouraged to submit because your proposal is outside the scope of NSF’s programs and funding opportunities described in the " Proposal and Award Policies and Procedures Guide ."

Even if you are discouraged from submitting, you always retain the option to submit a proposal. To submit, you can contact one of the points of contact identified on this page, or you can contact NSF at [email protected] or (703) 292-4840. NSF's points of contact are responsible for finding an appropriate mechanism for reviewing your proposal.

5. Is the merit review process less receptive to interdisciplinary proposals?

No. Funding interdisciplinary research is a high priority for NSF and, in turn, program officers will identify appropriate panelists and ad hoc reviewers to ensure that the full range of interdisciplinary research is covered by a proposal's reviewers.

But it is important to remember that being interdisciplinary does not automatically make a proposal more worthy. Unfortunately, NSF must decline a high percentage of meritorious proposals for a variety of reasons.

NSF's program officers have the responsibility and authority to recommend awards for proposals that were not among the most highly ranked by the review panels in order to maintain a balanced portfolio of investments.

6. If my funded interdisciplinary research project is not successful in achieving its stated goals, will this jeopardize future funding possibilities?

As with any prior NSF award, reviewers are asked to comment on the quality of prior work when evaluating a proposal. Note that your proposal may contain up to five pages to describe those results.

7. May I submit the same interdisciplinary research proposal to more than one program concurrently?

No. As indicated in NSF's " Proposal and Award Policies and Procedures Guide ," you are required to select one applicable program announcement, solicitation or program description when preparing your proposal. In some instances, you can also select more than one of NSF's programs or units that you feel are appropriate to co-review your interdisciplinary research project.

Even if you submit your proposal to one program, an NSF program officer may elect to have your proposal reviewed by more than one program.

8. If my interdisciplinary research proposal is reviewed by more than one program, will it be subject to "double jeopardy"?

Preliminary analyses indicate that proposals that are co-reviewed by two or more programs actually have, in most cases, a slightly higher chance of being recommended for funding than do proposals reviewed in a single program.

9. May I add extra pages to the project description because my proposal is interdisciplinary?

No. Your proposal must conform to the " Proposal and Award Policies and Procedures Guide " or to the limitations specified in the program solicitation.

10. How will differing program target dates, deadlines or submission windows affect the review of an interdisciplinary proposal that is reviewed by multiple programs?

This may lengthen the review process somewhat if one program's submission cycle differs substantially from another's. The points of contact identified on this site will assure that an appropriate review is carried out, and program officers will work together to conduct these reviews as expeditiously as possible.

June 10, 2015

Build Disciplinary and Interdisciplinary Research Skills

By Patrick McMahon

Discipline-specific research skills can be cultivated both through routine components of the advanced degree, such as required coursework, and other avenues, such as graduate internships. As you work to define and develop a research project, consider seeking relevant opportunities to build a diverse portfolio of research skills and methods.. As you progress toward completion of the degree, consider how you might translate research and data analysis skills into diverse career paths. For more guidance on translating your skills into diverse career paths, visit the Career Exploration and Preparation competency in this guide.

Steps You Can Take

Take on-campus courses.

Many departments offer formal training in the research methods associated with their discipline, allowing students to experiment with different approaches to answering research questions. Because these courses are often offered at an introductory level, it may be useful to revisit or sit in on a course you have already taken again in a later semester after having formulated an independent research project.

Particularly for students who work across disciplines, it may be relevant and useful to enroll in or audit methods courses offered in other fields. This is also a good way to broaden your skill-set in preparation for a variety of academic and non-academic careers. For instance, students in fields that rely primarily on quantitative data may benefit from taking a writing course in preparation for careers that require translating specialized findings for popular audiences or that broadly value strong communication skills. Similarly, many students in humanist and social science fields increasingly discover that their qualitative research and non-academic career preparation may be enhanced through the use of new digital and computational technologies.

Browsing the Berkeley  course catalog  will offer a sense of the wide variety of courses on offer at the University. Note that you may need the permission of the instructor to take a course in another department, and that it is best to request this permission well in advance of the beginning of the course.

Thanks to the Intercampus Exchange and Stanford-Berkeley Exchange  programs , graduate students with an excellent superior academic record may take a limited number of courses that are offered at Stanford or one of the other UC schools, and have the opportunity to make use of special facilities and collections and associate with scholars or fields of study not available on their home campus.

Take Time to Explore Scholarly Publications to Get an Overview of Diverse Research Approaches

While your department may specialize in a particular set of research approaches or methods, you may also wish to review other methods practiced by colleagues in the field, by academics in other disciplines, or (depending on your field) by practitioners associated with your field of study. Reviewing scholarly publications may inspire new research approaches or expand skills not necessarily honed in your home department, pinpointing new ways to distinguish and diversify your professional portfolio. The Library also offers  subject librarians who are available for consultation on particular research projects.

Participate in Working Groups and Attend On-Campus Lectures and Training Sessions

Advanced students may also wish to form research groups based on shared methods or questions that allow them to discuss the opportunities and issues associated with their approach. Creating and participating in research-based discussion groups can help not only to advance your research, but to cultivate leadership and collaboration skills valued in many professions. Some programs on campus, such as the  Doreen B. Townsend Center for the Humanities , have existing groups that you can join and provide support for new working groups .

The Berkeley  D-Lab  offers many resources for acquiring computational and technical skills, which are now broadly used across academic disciplines and various career paths. D-Lab training workshops focus on a wide range of topics, which in the past have included workshops on Text Analysis Fundamentals, Preparing Your Data for Qualitative Data Analysis, Introduction to Georeferencing, and Introduction to Artificial Neural Networks. They also regularly hold training workshops to build skills in a variety of platforms and programming languages, such as Excel, R, Python, and more. Find upcoming trainings and workshops on the D-lab’s Upcoming Workshops page .

The D-Lab also hosts a team of  consultants  who offer free appointments and drop-in hours for advising and troubleshooting on qualitative and quantitative research design, modeling, data collection, data management, analysis, presentation, and related techniques and technologies. Should you have advanced skills in these areas, consider applying to become a graduate consultant at D-Lab.

Participate in Lab Rotations

Many lab-based disciplines have formal programs of lab rotations that allow students to explore a potential research area and develop practical skills. The research rotation offers the opportunity to learn new experimental techniques, gain familiarity with different areas of research, experience the operating procedures of diverse types of labs, and identify mentors within the discipline. While the academic objective is to identify a lab in which to conduct dissertation research, skills gained on rotation can also provide relevant training for research projects and career prospects beyond the dissertation.

In recent years, some non-lab-based disciplines have found it useful to model their operations on the lab-based disciplines. If you are unsure, consider asking your advisors and faculty working in your research area if they have a lab group. For more on lab groups in the humanities, see “ Designing a Lab in the Humanities ,”  Chronicle of Higher Education  (2017).

Serve as a Graduate Student Researcher (GSR)

As in the lab rotation, participation in research projects as a GSR allows students to gain experience, identify strengths, and develop specialized interests. Work with your GSR supervisor to ensure that you are able to make the most of the opportunity: if you want to gain experience approaching the research question through the use of specific tools or methods, it is worth discussing the possibility with your research supervisor.

Be sure to keep track of the different skills you cultivate as part of the assistantship—when requesting recommendation letters to apply for jobs in subsequent years, it will be useful to remind your supervisor of the specific work you did for them. You may be surprised by how many of the disciplinary research skills honed in an assistantship correlate to desired qualifications for various professional positions and translate readily between academic and non-academic contexts. For examples, see Margaret Newhouse, “ Transferring Your Skills to a Non-Academic Setting ,”  Chronicle of Higher Education  (1998) and Stacy Hartman, “ Transferable Skills and How To Talk About Them ,”  MLA Connected Academics  (2016).

Complete Training in Responsible Conduct of Research

Your research may require you to protect the privacy of human subjects, to observe standards for research using animals, and/or to respect the rights of others to be recognized as contributors through proper citation, co-authorship, and obtaining copyright permissions. Online courses, workshops, and staff in the  Sponsored Projects Office  (SPO) can help you learn about these topics, and the Human Research Protection Program can answer questions about the process of getting approval for research with human subjects. 

Learning to use appropriate research methods and apply standards for responsible conduct provides practical experience for any future research-based career, but also engages broader critical-thinking skills about the ethics of research practices, protocols, and data analysis. The ability to conduct research responsibly in an academic setting testifies to the rigor and dedication that can make Ph.D.s appealing candidates for a variety of academic and professional careers.

Use Academic Breaks to Attend Intensive Skill-Building Programs

Some campus programs and centers offer high-intensity short-courses that take place during the spring or summer breaks. For instance, graduate students considering a career in industry or tech sometimes participate in summer bootcamps for coding or other technical skills, or participate in D-Lab summer trainings. These types of programs typically offer certificates of attendance or completion that should be listed (when relevant) on a CV or resume. In addition to the competencies they explicitly provide, they also attest to your ability to acquire a host of new skills in a short period of time.

Explore Bay Area Computational and Data Analysis Skill-Building Resources

As the home to Silicon Valley and multiple world-class universities, the Bay Area is an ideal location for those interested in learning, using, and building careers around computational and technical skills. Students looking to build computational or technical skills may also wish to participate in workshops or attend events at area hubs like the  Stanford Literary Lab  or the  UC Davis Postharvest Technology Center . Groups also exist for connecting locals with technical skills to burgeoning employment opportunities. For instance,  Tech SF  (a branch of the  Bay Area Video Coalition ) seeks to help unemployed tech professionals get the skills they need for a continually changing job market.

Take Advantage of Online Skill-Building Resources

Many discipline-specific, interdisciplinary, and generalist resources exist online for those seeking to expand their technical repertoire—particularly in the realm of computational skills. The  Institute for Digital Research and Education  offers resources, events, and consulting for UC-affiliates, including a wealth of materials accessible online.  BerkeleyX  provides free online courses in a variety of subjects for currently enrolled students, while sites like  Coursera ,  Code Academy  offer a mix of free and low-cost training sessions. Students employed by the University can also access many training videos and courses on LinkedIn Learning .

Students of color can explore the resources offered by the Institute in Critical Quantitative, Computational & Mixed Methods , which focuses on advancing scholars of color in data science and diverse methodologies.

Acquire Foreign Language Skills Relevant to Research

Certain fields may require students to acquire foreign language skills as part of their progress to degree. However, even when not required, students may wish to acquire new language skills independently, either as a supplement to their academic research or as a bridge to a variety of future careers. UC Berkeley offers instruction in over 80 languages, and fellowships such as the  FLAS  and  Fulbright  are available for graduate students undertaking language study. With its emphasis on the study of critical and less commonly taught foreign languages, the FLAS program is designed to lead into careers in university teaching, government service, or other employment where knowledge of foreign languages and cultures is essential. Participation in the Fulbright program, which offers an English Teaching Assistant program and fellowships for study and research abroad, opens up a wide variety of career paths for graduate students, including  foreign service , academia, and many more.

Do research universities specialize in disciplines where they hold a competitive advantage?

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• Published: 09 September 2024

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• Giovanni Abramo   ORCID: orcid.org/0000-0003-0731-3635 1 ,
• Francesca Apponi 2 &
• Ciriaco Andrea D’Angelo   ORCID: orcid.org/0000-0002-6977-6611 2  

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Enhancing the effectiveness and efficiency of national research systems is a top priority on the policy agendas of many countries. This study focuses on one aspect of the macroeconomic efficiency of research systems: whether research institutions specialize in scientific domains where they have a competitive advantage. To evaluate this, we developed a novel methodology. This methodology measures the scientific specialization indices of each organization in various research fields and assesses their relative research productivity. It then examines the correlation between these scores and between the resulting rankings. We applied this methodology to Italian universities. We found that a significant rank correlation between universities’ field specialization and their performance appears only in a few areas, and overall, the rankings are completely unrelated. Providing such data to research managers and policymakers can help inform strategies to enhance both micro- and macro-level efficiency.

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Introduction

The ability to generate novel knowledge and integrate it into innovative processes, products, and services plays a crucial role in sustaining socio-economic development within the current knowledge-based economy. Recognizing the pivotal role of research in fostering innovation and growth, various nations have invested in national research systems, making the improvement of their effectiveness and efficiency a top priority in their policy agendas.

A substantial body of literature has examined the scientific competitive standing of nations, highlighting the significance of research performance in the broader context (Aksnes et al., 2017 ; Allik et al., 2020 ; Bornmann & Leydesdorff, 2011 ; Harzing & Giroud, 2014 ; King, 2004 ; Li, 2017 ; May, 1997 ; Tijssen et al., 2002 ). Furthermore, the quality of human capital is identified as a vital factor for sustained growth (Aghion and Howitt, 2008 ; Romer, 1990 ; Lucas, 1988 ). Scientific knowledge accumulation contributes to the enhancement of educational and technological capabilities, positioning universities as key entities in the research system. Governments actively pursue the development and strengthening of higher education systems to globally compete for talented individuals and resources.

To assess and enhance the macroeconomic efficiency of research systems, governments conduct periodic evaluations of research institutions, often linking them to performance-based funding. Public funding tied to research performance serves as a competitive mechanism to incentivize continuous improvement in organizational efficiency. While there is a common agreement on the importance of incentive systems to foster research performance, an active debate is still ongoing regarding the methods to assess research performance. Critics of world and national university rankings have long argued against their indicators and methodologies (Billaut et al., 2010 ; Dehon et al., 2010 ; Liu & Cheng, 2005 ; Sauder & Espeland, 2009 ; Van Raan, 2005 ).

The dissatisfaction with evaluation methods among stakeholders of research systems has become so strong and widespread that it led the European Commission to promote the establishment of the Coalition for Advancing Research Assessment (CoARA), now consisting of over 600 research institutions, with the aim of reforming research assessment. CoARA envisions research assessment that “recognizes diverse outputs, practices, and activities, maximizing research quality and impact primarily through qualitative judgment supported by the responsible use of quantitative indicators”.

Reactions from evaluative scientometricians were swift (Abramo, 2024 ; Ioannidis & Maniadis, 2023 ; Torres-Salinas et al., 2023 ). Our personal position on the matter is that there is no one-size-fits-all methodological approach to conducting research assessment. The choice of methodology depends on various variables, including the objectives of the evaluation, scale, research disciplines, expected accuracy level, budget, data availability, and last but not least, context. We consider the contribution of scientometrics to research policy and management similar to that of medical imaging diagnostics in clinical medicine. The physician uses the results of imaging diagnostics together with other investigations deemed necessary to formulate a treatment. The same applies to the decision-maker in the field of research. Qualitative judgment can hardly do without quantitative assessment. The work illustrated in this manuscript is a case in point. It is unlikely that a qualitative analysis could provide the same information to the research decision-maker, despite the limitations and assumptions of the bibliometric method employed.

This study focuses on the microeconomic efficiency of research organizations, particularly their discipline portfolio management. Universities are viewed as “multi-business” organizations in the higher education sector, engaging in various scientific disciplines. The challenge for university managers (rectors) is the strategic management of their discipline portfolios, involving decisions on which disciplines to enter, dismiss, and invest in based on competitive standing. The study introduces a methodology to assess whether research organizations specialize in disciplines where they hold a competitive advantage, emphasizing the importance of aligning competitive standing with disciplinary specialization.

The operational methodology involves measuring scientific specialization indices and relative research productivity for each organization in each research field. The study aims to answer questions related to the efficiency of research organizations in choosing disciplines and exploiting their competitive advantages. The Balassa specialization index and fractional scientific strength indicator are applied to assess relative specialization and research productivity.

The application of the methodology is demonstrated using the Italian higher education system Footnote 1 as a case study. The choice of the Italian case is primarily driven by the availability of input data (cost of labor and capital) and output data (research output disambiguated at the individual level), which we use to measure the research performance of universities. In Italy, 98 universities have the authority to issue legally recognized degrees, with over 90% of faculty employed in public universities largely funded by the government (around 56% of total income). All professors are required to engage in both research and teaching. During the period under investigation, Italy ranked 8th globally in both the number of publications and citations. Italian scholars contributed 15.9% of total EU publications and received 19.3% of total EU citations. Despite a decrease in the number of academics, there has been significant growth in both the number of publications and their scholarly impact (Abramo & D’Angelo, 2023 ).

The results aim to reveal the efficiency of the discipline portfolio choices at both the organizational and system levels. The methodology can be extended to other countries contingent on data availability, providing valuable insights for university leaders and policymakers responsible for research system efficiency.

While existing literature explores scientific performance and research specialization separately, this study represents a novel attempt to examine the link between the two. The subsequent sections present the methodology and data, showcase the analysis results, and conclude with considerations for future work.

Data and methods

To evaluate the efficacy of organizations in selecting research fields for focused research activities, it is essential to quantify competitive advantage and specialization indexes in each field. These metrics facilitate the ranking of fields based on both indicators at each university, and the degree of similarity between the two rankings can be assessed using the tau-b Kendall correlation statistics (Conover, 1999 ; Kendall, 1938 ). This correlation coefficient attains a value of 1 when there is perfect agreement between the two rankings, signifying maximum efficiency in the research organization’s selection of scientific domains for investigation. A correlation coefficient of 0 indicates no correlation between rankings, while a value of − 1 signifies maximum inefficiency, with one ranking being the exact reverse of the other.

To gauge the competitive advantage of a research organization in each field, we compare its research productivity with that of all other observed organizations in that field. We define the productivity of researchers in a field as the output value per euro spent on research. Additionally, we assess relative research field specialization by comparing the organization’s share of research expenditures in the field with that of all organizations.

To conduct these assessments, access to the following information is required: (i) the research staff in each organization; (ii) their classification per research field; (iii) their individual cost; (iv) the cost of resources other than labor devoted to research in each field, and (v) the research output in each field.

In Italy, the MUR maintains a database of university personnel, providing detailed information on each professor, including name, gender, affiliation, field classification, and academic rank at the end of each year. Footnote 2 Professors are classified into 370 scientific disciplinary sectors (SDS) grouped into 14 university disciplinary areas (UDAs). Footnote 3 Data on salary costs for research personnel are available from the DALIA database Footnote 4 maintained by the MUR. However, the cost of resources other than labor at the discipline level is scarcely available globally. Still, for this study, we assume it to be similar to data available in Norway Footnote 5 and invariant across professors. Footnote 6

Publications indexed in the Web of Sciences (WoS) serve as a proxy for the total output of research activities. The bibliometric dataset is obtained from the Italian Observatory of Public Research (ORP), a database developed and maintained by the authors. This dataset is derived under license from WoS, utilizing a complex algorithm for affiliation reconciliation and author disambiguation. Footnote 7

Due to the limited coverage of bibliometric repertories in the arts and humanities and several social science fields, the analysis is confined to STEM disciplines, encompassing 205 SDSs in 10 UDAs. To ensure statistical significance at the field level, only university-SDS pairs with a minimum of five observations (i.e., five professors in the SDS of the university) are considered. For each university-SDS pair, two indicators are calculated: a proxy of research productivity, the Fractional Scientific Strength (FSS), representing relative competitive advantage, and the research field specialization index (SI), which is elucidated in the subsequent subsections. To ensure accuracy in impact measurement, a minimum two-year citation window is allowed, and the observation period spans from 2015 to 2019, with citations counted as of December 31, 2021.

Measuring the competitive advantage of universities

We consider research laboratories as productive entities with production factors consisting of i) researchers (L); tangible resources (such as scientific instruments, materials, etc.), and intangible resources (like prior knowledge, social networks, etc.) (K). Researchers generate knowledge, which is documented in publications (Q) to facilitate its dissemination. The value of publications varies based on their impact on future scientific advancements, commonly referred to by bibliometricians as scholarly impact, measured through citation-based metrics. Productivity, a key indicator of the efficiency of any production system, is operationalized through several simplifications and assumptions. Initially, scientific productivity is measured at the individual level using the FSS, Footnote 8 defined as:

w  = average yearly salary of the professor (we halve labor costs, assuming that 50 percent of professors’ time is allocated to activities other than research).

k  = average yearly capital available for research to the professor.

t  = number of years of work by the professor in the period under observation.

N  = number of publications by the professor in the period under observation.

\({c}_{i}\) = impact of publication i (weighted average of the field-normalized citations received by publication i and the field-normalized impact factor of the hosting journal) Footnote 9 ;

\({f}_{i}\) = fractional contribution of professor to publication i .

As for the cost of labor, w, data concerning salary for research personnel were obtained from the DALIA database, Footnote 10 which is also maintained by the MUR. As for the cost of capital, k, we relied on Abramo et al. ( 2020 ). Footnote 11

The productivity of universities, which are heterogeneous in the research fields of their staff, cannot be directly measured at the aggregate level. So, after measuring the productivity of individual professors (Eq.  1 ) we normalize individual productivity by the average of the relevant field (SDS). At the aggregate level then, the yearly productivity FSS A for the aggregate unit A (SDS, UDA, Department, etc.) is:

\(RS\) = number of professors in the unit, in the observed period;

\({FSS}_{{P}_{j}}\) = productivity of professor j in the unit;

\(\overline{{FSS }_{P}}\) = average productivity of all productive professors under observation in the same SDSs of professor j .

A value of \({FSS}_{A}=1.20\) means that the university’s unit A employs researchers with average productivity of 20% higher than expected.

In this way, we can measure the productivity of the university at SDS, UDA, and the overall level.

Measuring the research field specialization of universities

We draw from international trade theory, adapting the concept and measure of production specialization to our context. To assess the universities’ degrees of specialization in each field, we use the Balassa index (Balassa, 1965 ). It shows whether a university specializes in a specific field relative to other universities. Named PFTC the total cost of the production factors L and K, employed by the university i in SDS j , in the observation period:

where M j is the number of professors of university i in SDS j ; and \({t}_{z}\) the number of years on staff of professor z in the observation period, the specialization index \({SI}_{ji}\) of university i , in the SDS j is:

The higher the value of \({SI}_{ji}\) compared to one, the more specialized the university i is in SDS j . If \({SI}_{ji}\) is less than one, it means that no specialization is involved in j for university i . At a more aggregate level of UDA, the index can be easily calculated by applying Eq.  4 with j referring to UDA instead of SDS.

In order to answer the research questions, we measure for each university the degree of similarity of the SDS rankings where the university does research for the two indicators just described. For instance, we present the case of the University of Rome “Sapienza,” the largest nationally (and in Europe) with over 3500 professors on staff on 31/12/2021. In this study, the analysis dataset is limited to STEMs, where we count 2905 professors on staff during the observation period, in 180 different SDSs.

Figure  1 shows the scatterplot of the 147 SDSs with at least five professors on staff. The two indicators show values greater than unity in only 22 SDSs (accounting for 15 percent of the total). In 48 SDSs (32.7 percent), both indicators are below 1, but the most crowded quadrant is the second one, with 60 SDSs showing values of FSS less than unity and SI greater than unity simultaneously. The two rankings show virtually zero correlation (Kendall’s tau-b = − 0.025), allowing us to state that at the overall level, this university does not specialize in research fields where it holds a competitive advantage.

FSS and SI distributions for 147 SDSs of University of Rome “Sapienza”

Repeating the analysis for all universities in the dataset, we obtain what is shown in Table  1 . Kendall’s correlation coefficient is positive and significant only for the University of Sannio (tau-b = 0.800, calculated for only 5 SDSs). In particular, this university shows very high productivity (FSS) in ING-INF/05 (Information processing systems), ranking at 86th national percentile, and very low in FIS/01 (Experimental Physics) ranking at 17th national percentile. At the same time, the specialisation index recorded for the first SDS is the highest among the five active in the university, while for the second it is the lowest.

At the bottom of the list of Table  1 , we find the University of Urbino “Carlo Bo”: the correlation recorded for the values of the two variables measured on its 13 SDSs is negative and significant (tau-b = − 0.487). In this University, FIS/01 (Experimental Physics) is the top ranked SDS by productivity (top at national level by FSS) but the bottom ranked, among the 13 active SDSs in the university, by SI. Conversely, the University registers the worst FSS performance in GEO/05 (Applied geology), an SDS with the second highest SI (7.261), immediately after GEO/02 (Stratigraphic and sedimentary geology) registering a value of SI equal to 8.057.

Overall, 30 universities show positive correlation coefficients (in no case significant, apart from Sannio) against the 31 universities that show a negative value. However, in no case it is significant apart from the University of Urbino, the University of Calabria (tau-b = − 0.200), and Cattolica del Sacro Cuore (tau-b = − 0.208). In these three cases, we can say that these universities concentrate their research activity in fields with relatively modest performance. The middle zone of the distribution is very dense, with 35 universities (almost half of the total 63), and a correlation coefficient within the range (− 0.1; + 0.1). We deduce that, apart from a single university, Italian universities do not specialize in fields where they hold a competitive advantage. Scrolling the top of Table  1 , we notice the presence of “Scuole Superiori” which are known to be exceptionally brilliant in terms of scientific performance. We then ask whether there is a correlation between “selection efficiency” and research productivity. In other words, whether the best-performing universities are also the most efficient in choosing fields in which to focus their research activities. The scatterplot in Fig.  2 reports the position of each university by overall productivity (FSS) and degree of similarity for ranking (FSS-SI Kendall correlation). To assess the FSS-degree of similarity ranking correlation, we apply the Somers’ D statistics, which reveal no correlation (Somers’ D index = 0.0036, P  >| z |= 0.972).

Scatterplot of overall productivity vs FSS-SI correlation for universities in the dataset

It would thus seem that not even the most productive universities are particularly careful in choosing those research fields in which they can exploit their competitive advantage. Except for only one university, among others characterized by a low number of observations (5 SDSs only), there is no correlation between the fields in which universities specialize and the relative research productivity; indeed, in some cases there is an inverse correlation. However, there may “locally” be fields in which this occurs. To test it, we consider fields rather than universities as the unit of analysis. The question we then ask is: which fields are characterized by greater (lesser) selection efficiency?

At the operational level, for each university, we sort the SDSs in which it is active by FSS and SI and measure the respective percentiles (100 = top). Footnote 12 An SDS with a 90 percentile by FSS means that only 10% of the SDSs in which the university is active perform better. Similarly, an SDS with the 90 percentile by SI means that it is more specialized in only 10% of the SDSs in which the university is active. At this point, for each SDS, we can construct a scatterplot by the above two percentile rankings of all universities active in that SDS and apply correlation statistics.

For instance, we report the case of Applied Technological Pharmaceutics (CHIM/09), in which 24 universities (with at least 5 professors) conduct research. Figure  3 reports the scatterplot of the data and shows a weak but significant correlation, with Kendall’s tau-b values of 0.294 (Prob >| t |= 0.0471). There are eleven universities in the first quadrant, with both FSS and SI percentiles at least equal to 50. In the third quadrant, there are six universities with both indicators below the 50th percentile. In the second quadrant, the position of the University of Salerno stands out, showing an FSS percentile of 24.3 compared with an SI percentile of 73.0. In the fourth quadrant, on the other hand, we have the opposite anomaly, that of the University of Florence, which in this SDS shows relative productivity at the top 3 percentile (FSS percentile of 97.0) against, however, a very low specialization percentile of 13.9. Conversely, in Biochemistry (BIO/10) the correspondence between the two dimensions is much less evident. As shown in Fig.  4 , in this SDS, only four out of 44 universities are positioned in the first quadrant. The set of universities (16 in all) is far more numerous in the third quadrant. Of the remaining 24, 21 universities are in the fourth quadrant, characterized by a high relative value of performance (FSS percentile greater than 50) and low relative value of specialization (FSS percentile greater than 50), resulting in an overall correlation value close to zero in fact (Kendall’ tau-b = − 0.074).

FSS and SI percentiles distributions for 24 universities in CHIM/09, Applied Technological Pharmaceutics

FSS and SI percentiles distribution for 44 universities in BIO/10, Biochemistry

Repeating the analysis for all SDSs under observation yields the data shown in Table  2 . For the sake of significance, we limit the analysis to SDSs (173 out of the total 205) with at least five observations, i.e., at least five national universities with at least five professors in the SDS under consideration from time to time. Overall, in only 12 SDSs (accounting for 7% of the total), we obtain positive and significant correlation values (Kendall tau-b): 4 SDSs are in the Agricultural and veterinary sciences and 3 in Chemistry. At the same time, the data indicate 7 SDSs in which the correlation is significant but negative, mainly in the Medicine area.

Discussion and conclusions

Because of its abstract nature, knowledge evaluation is challenging for scholars, practitioners, research managers, and policymakers. Over time, bibliometricians have tried to propose, apply, validate and improve indicators and approaches to identify the strengths and weaknesses of research systems at macro (country), meso (institutions), and micro (individuals) levels. There is a particular interest in how institutions and countries perform in scientific disciplines and what determinants explain why a country presents a specific competitive advantage in one field over another (Braun et al., 1995 ; Horta & Veloso, 2007 ; King, 2004 ; Kozlowski et al., 1999 ).

In this study, we investigated a complementary aspect of the microeconomic efficiency of research organizations and the macroeconomic efficiency of research systems i.e., whether research institutions specialize in scientific domains where they hold a competitive advantage. We measured the scientific specialization index of each Italian university and their research productivity in each field. Measuring research productivity (defined as an output-to-input ratio) is a formidable task because of the lack of input data. Benefiting from Italian structural advantages concerning input metadata availability, we have operationalized the measurement of a proxy of productivity unparalleled worldwide.

We applied Kendall’s statistics to assess the degree of similarity between the rankings by research productivity and specialization index of Italian universities in each research field. Findings reveal that, with only one exception, Italian universities do not specialize in fields where they hold a competitive advantage. In particular, the data show the presence of three universities that concentrate research activity in fields where they even have a relatively modest performance.

This result, in part, anticipates the answer to the second research question we initially posed, whether the organizations best at doing research were the ones best at selecting the fields to concentrate their research activities. The answer is negative: the analysis finds a complete absence of correlation between the overall productivity of universities and their ability to concentrate in the fields in which they are best at doing research.

The final in-depth study to answer the third research question aimed to identify the fields in which the most productive organizations concentrate their research activities. As was to be expected, even in this case, few positive exceptions emerged from a rather apparent general phenomenon: in only 12 fields out of the 173 analyzed, there is, in fact, a significant positive correlation between the productivity of universities and their degree of specialization in the field. These fields fall mainly in two disciplines, Agricultural, and veterinary sciences and Chemistry. In seven other fields, the correlation is significant but negative, indicating the paradox of a greater concentration of research by lower-performing universities.

In interpreting the main findings of the analysis, it should not be forgotten that universities play the primary role in higher education in addition to research. This aspect implies a necessary diversification in research activity. Delivering degrees in, e.g., engineering implies giving courses and hiring relevant professors in mathematics, physics, and others. In an efficient research system, one expects that universities excelling in engineering research, while performing low in mathematics, deliver degrees in engineering, leaving to others those in mathematics, but not the other way around. The evidence from the analysis is both suggestive and counterintuitive. However, this evidence may find an explanation considering the peculiarities of the Italian academe, which was for years a scarcely competitive higher education system.

It was only in 2009, following the first national research evaluation exercise, that Italy began allocating a small portion of its public funding to universities based on research performance. Initially, this was around 7%, but law 98/2013 set a minimum share of 16% for 2014, with a mandated annual increase of 2% up to a maximum of 30%. Despite this, the system remains somewhat erratic. While the funding is awarded based on the average research performance of individual professors, the financial rewards are given to the universities, which are not required to distribute the money according to individual performance, that is not communicated to them. This lack of obligation or specific incentives allows universities to hire or promote professors in fields where they may not necessarily excel.

The reasons underlying the revealed low efficiency in the choice of disciplines to concentrate research in, are partly to be found in the interplay of the management culture that has dominated for years in the academia, and Italian labor laws. Universities have developed in a non-competitive environment, whereby public funds were allocated to them on the basis of size and type of disciplines, and professors’ salaries were (and still are) not linked to performance. Rectors are elected by both academic and non-academic staff, fostering please-all management practices to assure re-election. Resources for recruitment have been allocated internally to the various departments more on the basis of their negotiation power than inspired by the principle of efficiency (Civera, D’Adda, Meoli, Paleari, 2022 ). Furthermore, the effectiveness of recruitment and career progress has been undermined by amply diffused favoritism practices which often prevail on merit-based selection (Abramo et al., 2014a , 2014b , 2015 ; Durante, Labartino, Perotti, 2011 ; Gerosa, 2001 ; Zagaria, 2007 ). In general, non-competitive environments do not favor the application of management science theory in running organizations. We then doubt the mastering of business portfolio management techniques among the government bodies of Italian universities.

Even where the willingness to change the bad practices of the past where there, following the university performance-based funding recently introduced by the government, good-willing rectors find the current labor law a formidable obstacle to dismissing inefficient professors, and pursuing discipline portfolio efficiency. While possible in theory it reveals hardly realizable in practice.

Inefficient diversification strategies by universities translate into inefficient research systems at national level. The recent introduction of performance-based funding linked to the national research assessment exercises has been an important initial step by the government toward the strengthening of a competitive environment, a harbinger of continuous improvement along the knowledge production dimension. Additional incentives are needed to stimulate efficiency also along the discipline portfolio management dimension. We interpret the recent introduction of extra-financial rewarding for “excellent” university departments as assessed by the national research assessment exercises, an important step by the government toward this direction. The government’s direct allocation of resources to the best performing disciplinary departments in each university, might counterbalance the internal political power influence in determining the disciplinary areas in which to recruit scientific personnel. A concern remains though about the VQR methodological failures in assessing research performance and, consequently, about the correct ranking of university departments (Abramo & D’Angelo, 2015 ).

An additional intervention that we deem effective would be the communication to each university of their diversification strategy efficiency, and recommend that the government allocation of resources be based not only on the efficiency of production but also of discipline portfolio management.

We conclude the work by reminding the usual limits and assumptions embedded in all bibliometric approaches. Firstly, the knowledge generated may not always be reflected in publications, and bibliographic databases like WoS, utilized in this study, may not encompass all published works. Secondly, assessing the impact of publications through citation-based metrics is a predictive rather than definitive measure, and citations only verify scholarly impact while overlooking other forms of impact. Thirdly, we do not account for variations in capital available to individual researchers. Finally, the results could be impacted by the classification schemes used for publications and professors. These constraints highlight the necessity for caution when interpreting data obtained from scientometric methods. However, we do not expect these limitations to disproportionately affect any particular Italian university, thereby preserving the reliability of the study’s findings.

For further insights into the Italian higher education system, refer to Abramo et al. ( 2012 ).

http://cercauniversita.cineca.it/php5/docenti/cerca.php , last accessed on 15 July 2024.

The complete list is accessible on attiministeriali.miur.it/UserFiles/115.htm, last accessed on 15 July 2024.

https://dalia.cineca.it/php4/inizio_access_cnvsu.php , last accessed on 15 July 2024.

http://www.foustatistikkbanken.no/nifu/?language=en , last accessed on 15 July 2024.

Any variances do not exert a substantial impact on the ultimate outcomes since all comparisons are executed at the field level. An alternative approach would involve overlooking the parameter k, as is common in many studies. However, this would be tantamount to assuming k  = 0. Such a scenario is further from reality than assuming equivalent values of k in both Italy and Norway.

The F-measure, representing the harmonic average of precision and recall for authorship disambiguation performed by the algorithm, stands at approximately 97%, with a margin of error of 2% and a 98% confidence interval.

For a comprehensive explanation of the methodology, underlying theory, assumptions and limitations, as well as the data source, we direct the reader to Abramo and D’Angelo ( 2014 ) and Abramo et al. ( 2020 ).

This combination serves as the most accurate projection of future long-term citations for a publication (Abramo et al., 2019 ). Citations are adjusted to the mean of the distribution concerning all referenced publications from the same year and the Web of Science subject category (SC) of publication i . The journal’s impact factor (IF), corresponding to the year of publication, is normalized relative to the average of the IF distribution of all journals in the same SC of publication i.

Table 4 in Abramo et al. ( 2020 ) compiles information on the cost of capital ( k ), the total cost of production factors ( w /2 +  k ), and normalization factors for total cost across academic ranks and disciplines. The normalization factor utilized in Eq. ( 1 ) corresponds to the lowest recorded total cost, observed for assistant professors in Psychology (54,081 Euro). In the subsequent analysis, we will employ these total cost normalization factors to present measures of productivity.

Again, for the sake of significance we will only consider universities with at least five SDSs, each with at least five professors.

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The contribution of each author is specified as follows: Conceptualization: GA, CAD. Methodology: GA, CAD. Investigation: GA, FA, CAD. Data curation: FA, CAD. Visualization: FA, CAD. Supervision: GA, CAD. Writing—original draft: GA, FA, CAD. Writing—review and editing: GA, CAD.

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Giovanni Abramo and Ciriaco Andrea D’Angelo are members of the Distinguished Reviewers Board of Scientometrics. The authors declare that they have no further conflicts of interest.

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Abramo, G., Apponi, F. & D’Angelo, C.A. Do research universities specialize in disciplines where they hold a competitive advantage?. Scientometrics (2024). https://doi.org/10.1007/s11192-024-05136-7

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Received : 12 December 2023

Accepted : 12 August 2024

Published : 09 September 2024

DOI : https://doi.org/10.1007/s11192-024-05136-7

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