In recent years, science policy experts have been promoting interdisciplinary research (IDR) in order to foster innovation and address grand scientific challenges. But to date we know little about whether, how, and to what extent universities are committed to fostering this type of research. This paper develops the first measure of university commitment to IDR, which relies on the organizational structuring of research activity into research centers and departments. We extend the previous literature by measuring, rather than assuming, the interdisciplinary nature of research units. Using a large amount of textual data from 157 research universities in the United States, and combining machine learning and confirmatory factor analysis techniques, we develop a continuous and composite measure that taps universities’ structural commitment to IDR. We then examine the commitment exhibited by specific universities and how such commitment varies by university characteristics like size, resources, and region. Results show that the fraction of centers and departments that are interdisciplinary is critical to measuring a university’s structural commitment to IDR and to developing specific research policies aimed at fostering IDR.
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Following Klein’s (1990) lead, some scholars have taken pains to distinguish between interdisciplinary, multidisciplinary, and cross-disciplinary efforts (Holley 2009). However, like Brint et al. (2009), Porter et al. (2007), and Wagner et al. (2011), we do not distinguish them empirically and acknowledge that our measure of IDR may very well include multidisciplinary and cross-disciplinary efforts.
Porter and colleagues’ integration measure is equivalent to Rao-Stirling index (Rafols and Meyer 2010). To gauge a paper’s degree of interdisciplinarity, it focuses on its works cited, and incorporates not only the number of fields cited (variety), but also the uniformity of the distribution of fields (evenness), and the relatedness (dissimilarity). Papers whose bibliographies represent a large number of fields that are evenly distributed and unrelated have high levels of interdisciplinarity.
Leahey et al. (2017) contend that the pooled approach better captures multidisciplinarity (simply drawing from two or more fields), and the averaging approach betters captures interdisciplinarity (actually integrating two or more fields), but conceptualization and measurement on this front deserves more attention. Like others (Jacobs and Frickel 2009), we do not distinguish between the two.
We exclude internal grants partly for pragmatic reasons: our initial investigations revealed that there is no comprehensive and standardized source for internal grants across the 157 universities we study. We exclude cluster hiring initiatives to boost measurement validity: while cluster hiring captures commitment to (and not just engagement with) IDR, some cluster hiring initiatives are intended to foster diversity more than interdisciplinarity (Staff 2015). Given our focus on research, we also exclude university efforts to promote interdisciplinary education and training, which others have studied (Brint et al. 2009; Hackett and Rhoten 2009; Holley 2009, 2015). Moreover, most of the science policy interest in interdisciplinarity revolves around research rather than teaching.
We find no significant differences between public and private universities in their average number of departments (80 vs. 73, p = 0.18) or in the fraction of their departments that are interdisciplinary (0.49 vs. 0.49, p = 0.92). We also compared universities that are in the bottom percentile (10th, 20th, and 30th) on inflation adjusted total revenues per FTE to more well-resourced universities. Regardless of the cut-off used, we found that poorer universities do indeed have fewer departments. Part of this is attributable to the fact that only one of the poorer universities has land grant status, and land grant universities have more departments given their broad mission. However, poorer universities do not have a greater relative number of interdisciplinary departments. So, although poor universities may well be merging departments for efficiency’s sake, the resulting departments are not more likely to be interdisciplinary. This suggests that when they do consolidate, they merge cognate departments together.
Jacobs (2013: 215) notes, for example, that Arizona State University’s reorganization actually resulted in the division of departments like Sociology, whose members were dispersed across the School of Social and Family Dynamics and the School of Evolution and Social Change.
To ascertain this, we selected four universities from our sample and identified their research centers from two sources: the Gale Directory and manual web searches. We chose universities that differed by region, sector, and prestige. We manually coded whether centers from each search technique were: (1) active (2) engaged in research and (3) interdisciplinary. We then made formal comparisons between the two sources using cross-tabulations and Chi square test statistics. We found that web searches were more subject to systematic differences in reporting of research centers across universities, and resulted in the inclusion of more defunct or non-research oriented centers, compared to Gale searches. We found no significant differences in the association between search technique and the likelihood of a center to be interdisciplinary. Thus, we determined Gale to be the preferable source of information, albeit more conservative than web searches. Details about these validity checks are available upon request.
We had hoped to rely on Brint et al.’ (2009) classification (which counts fields as interdisciplinary if they drew faculty from two or more disciplines and if they were classified as interdisciplinary by more than two-thirds of the universities he studied) but his research team could not locate the list. We considered supplementing the department name with additional text from faculty members’ publications, but this would likely tap engagement rather than commitment to interdisciplinary research. We also considered supplementing the department name with additional text from department websites, but this proved challenging to do for the year of interest (2012–2013) even with the Internet Archive’s WayBack Machine. In the end, these potential supplements proved unnecessary because we were able to build a precise classifier based on department name alone.
The six schools are the Colorado School of Mines, Illinois Institute of Technology, University of North Carolina at Greensboro, Wake Forest University, George Mason University, and University of Texas at Dallas.
For example, for RCs, we compute the proportion RCs that fall in each category of classification (interdisciplinary and not interdisciplinary). Then, we use the precision rate determined in the first stage of machine learning to calculate the likelihood of misclassification for each category. That is, for a binary outcome, we calculate the percent of (manually coded) interdisciplinary RCs that were correctly and incorrectly classified by the machine, and the percent of non-interdisciplinary RCs that were correctly and incorrectly classified. Then, we use these percentages to correct the raw ratios. For example, if 36% of departments were classified as not interdisciplinary, but our precision rates tell us that 2% of departments coded as ‘interdisciplinary’ should be classified as ‘not interdisciplinary,’ we add 2 to 36%.
Universities with medical schools have 70 RCs on average, a significant difference from universities without medical schools, which have 47 RCs on average. There is no significant difference in the fraction of RCs that are interdisciplinary between these two groups.
Technical universities’ lower structural commitment to IDR cannot be attributed to any bias on the part of our classifier to excessively tag humanities and social science fields as interdisciplinary. Indeed, when we examine the percent of research centers tagged as interdisciplinary across Gale’s macro fields, we find that the highest values occur in Engineering and Technology, Physical and Earth Sciences, Astronomy and Space Sciences, and Computers and Mathematics, whereas lower values occur in Government and Public Affairs, Law, and Education.
Recall that we view the presence and nature of these organizational units, regardless of the motivation behind their development, as indicative of structural commitment to IDR.
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This research was supported by NSF SciSIP Collaborative Grants to Erin Leahey and Sondra Barringer (Award #s 1461989 and 1461846). Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. We are grateful to Steven Brint, Scott Frickel, and Jerry Jacobs for their foundational work, and to Karina Salazar and Esme Middaugh for impeccable research assistance.
Appendix: Coding guidelines
Appendix: Coding guidelines
These abbreviated manual coding guidelines were developed based on NSF’s disciplinary classifications (https://www.nsf.gov/statistics/nsf13327/pdf/tabb1.pdf), CIP codes (https://nces.ed.gov/ipeds/cipcode/browse.aspx?y=55), and the foundational work of other scholars, especially Brint et al. (2009). These coding guidelines were also incorporated as features into the machine classifier to guide its classification of text. The full version is available upon request.
The text is likely interdisciplinary if it references….
Interdisciplinarity, including terms like:
“interdisciplinary,” “multidisciplinary,” “trans-disciplinary,” “integrative,” “synthesis,” “applied,” “cross-disciplinary,” and “integration.”
Two or more disciplines (i.e., CIP and NSF broad categories), or their stems, like:
“Center for Pharmacology and Physiology,”
“Department of Sociology & Criminology”
Environmental or earth sciences, like:
“Institute of Environmental Policy,” “Atmospheric and Oceanic Sciences”
Any of the following stem words in combination with “studies”
America-, biblic-, cultural, Islam-, sustain-, community, Slavic, rehab-, peace…
Professional schools, like:
Medicine, Nursing, Social Work, Education, Public Health, Law, Business, Public
Inherently interdisciplinary fields, like:
Space science, demography, gerontology, criminal justice, ethics
Sexual minorities or women, such as:
“women,” “gender,” “feminist,” “sexuality”
Ethnic/racial minorities, such as:
“African American,” “Chicano,” “Hispanic,” “American Indian,” “Asian American”
Area/region/period/religion studies, like:
“Institute of Africana/African Studies,” “Department of Latin American Studies”
International or global orientation
“International Relations” or “Center for Global Studies”
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Leahey, E., Barringer, S.N. & Ring-Ramirez, M. Universities’ structural commitment to interdisciplinary research. Scientometrics 118, 891–919 (2019). https://doi.org/10.1007/s11192-018-2992-3
- Research centers
- Machine learning
Mathematics Subject Classification
- 28 Measure & Integration
- 62 Statistics
- 68 Computer Science
- C38 Classification Methods
- Principal Components
- Factor Models
- I23 Higher Education
- Research Institutions