Research topic displacement and the lack of interdisciplinarity: lessons from the scientific response to COVID-19

Eva Seidlmayer (), Tetyana Melnychuk (), Lukas Galke (), Lisa Kühnel (), Klaus Tochtermann (), Carsten Schultz () and Konrad U. Förstner ()
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Eva Seidlmayer: ZB MED - Information Centre for Life Sciences
Tetyana Melnychuk: Kiel University
Lukas Galke: Max Planck Institute for Psycholinguistics
Lisa Kühnel: ZB MED - Information Centre for Life Sciences
Klaus Tochtermann: ZBW - Leibniz Information Centre for Economics
Carsten Schultz: Kiel University
Konrad U. Förstner: ZB MED - Information Centre for Life Sciences

Scientometrics, 2024, vol. 129, issue 9, No 4, 5179 pages

Abstract: Abstract Based on a large-scale computational analysis of scholarly articles, this study investigates the dynamics of interdisciplinary research in the first year of the COVID-19 pandemic. Thereby, the study also analyses the reorientation effects away from other topics that receive less attention due to the high focus on the COVID-19 pandemic. The study aims to examine what can be learned from the (failing) interdisciplinarity of coronavirus research and its displacing effects for managing potential similar crises at the scientific level. To explore our research questions, we run several analyses by using the COVID-19++ dataset, which contains scholarly publications, preprints from the field of life sciences, and their referenced literature including publications from a broad scientific spectrum. Our results show the high impact and topic-wise adoption of research related to the COVID-19 crisis. Based on the similarity analysis of scientific topics, which is grounded on the concept embedding learning in the graph-structured bibliographic data, we measured the degree of interdisciplinarity of COVID-19 research in 2020. Our findings reveal a low degree of research interdisciplinarity. The publications’ reference analysis indicates the major role of clinical medicine, but also the growing importance of psychiatry and social sciences in COVID-19 research. A social network analysis shows that the authors’ high degree of centrality significantly increases her or his degree of interdisciplinarity.

Keywords: COVID-19; Bibliometrics; Interdisciplinarity; Research dynamics; Network analysis; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11192-024-05132-x

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