A topic model approach to measuring interdisciplinarity at the National Science Foundation
As the National Science Foundation (NSF) implements new cross-cutting initiatives and programs, interest in assessing the success of these experiments in fostering interdisciplinarity grows. A primary challenge in measuring interdisciplinarity is identifying and bounding the discrete disciplines that comprise interdisciplinary work. Using statistical text-mining techniques to extract topic bins, the NSF recently developed a topic map of all of their awards issued between 2000 and 2011. These new data provide a novel means for measuring interdisciplinarity by assessing the language or content of award proposals. Using the Directorate for Social, Behavioral, and Economic Sciences as a case study and drawing on the new topic model of the NSF’s awards, this paper explores new methods for quantifying interdisciplinarity in the NSF portfolio.
KeywordsInterdisciplinarity Topic model Network analysis
The author would like to thank Dave Newman from the University of California, Irvine for his assistance with the topic model data; Myron Gutmann, Amber Story, and many others at the NSF for their perspectives and advice on this paper; the Sci2 Team for the use of their Science of Science Tool; Julia Lane who was until recently the Program Director for the Science of Science Policy Program for her vision and work in developing the NSF Portfolio Explorer; and the anonymous reviewers of this paper for their constructive comments and helpful suggestions.
This work was completed as part of a AAAS Science and Technology Policy Fellowship and supported by the National Science Foundation (Award 1035631). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
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