Methods for Measuring Social and Conceptual Dimensions of Convergence Science

Research Evaluation 32, 256-272 (2023). DOI:10.1093/reseval/rvad020

23 Pages Posted: 1 Jun 2022 Last revised: 30 May 2023

See all articles by Alexander Michael Petersen

Alexander Michael Petersen

University of California Merced, Department of Management of Complex Systems

Felber Arroyave

University of California, Merced

Ioannis Pavlidis

University of Houston

Date Written: May 30, 2023

Abstract

Convergence science is an intrepid form of interdisciplinarity defined by the US National Research Council as “the coming together of insights and approaches from originally distinct fields” to strategically address grand challenges. This paradigm has been promoted extensively in the last decade, becoming a model for design- ing flagship research programs that strategically address grand challenges. Despite its increasing relevance to science policy and institutional design, there is still no practical framework for measuring convergence. We address this gap by developing a measure of disciplinary distance based upon disciplinary boundaries delin- eated by hierarchical ontologies. We apply this approach using two widely used ontologies – the Classification of Instructional Programs (CIP) and the Medical Subject Headings (MeSH) – each comprised of thousands of entities that facilitate classifying two distinct research dimensions, respectively. The social dimension codifies the disciplinary pedigree of individual scholars, connoting core expertise associated with traditional modes of mono-disciplinary graduate education. The conceptual dimension codifies the knowledge, methods, and equip- ment fundamental to a given target problem, which together may exceed the researchers’ core expertise. Con- sidered in tandem, this decomposition facilitates measuring social-conceptual alignment and optimizing team assembly around domain-spanning problems – a key aspect that eludes other approaches. We demonstrate the utility of this framework in a case study of the human brain science (HBS) ecosystem, a relevant convergence nexus that highlights several practical considerations for designing, evaluating, institutionalizing and accelerating convergence. Econometric analysis of 655,386 publications derived from 9,121 distinct HBS scholars reveals a 11.4% article-level citation premium attributable to research featuring full topical convergence, and an additional 2.7% citation premium if the social (disciplinary) configuration of scholars is maximally aligned with the topical configuration of the research.

Keywords: convergence, team science, team assembly, evaluation, ontology, interdisciplinary distance

JEL Classification: O52, O51, O53, O31, O32, F02, C33, Q55, D85

Suggested Citation

Petersen, Alexander Michael and Arroyave, Felber and Pavlidis, Ioannis, Methods for Measuring Social and Conceptual Dimensions of Convergence Science (May 30, 2023). Research Evaluation 32, 256-272 (2023). DOI:10.1093/reseval/rvad020, Available at SSRN: https://ssrn.com/abstract=4117933 or http://dx.doi.org/10.2139/ssrn.4117933

Alexander Michael Petersen (Contact Author)

University of California Merced, Department of Management of Complex Systems ( email )

School of Engineering
Science & Engineering 2, Suite 315
Merced, CA 95343
United States

Felber Arroyave

University of California, Merced ( email )

P.O. Box 2039
Merced, CA 95344
United States

Ioannis Pavlidis

University of Houston

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
147
Abstract Views
920
Rank
382,622
PlumX Metrics