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Research Topic Displacement and the Lack of Interdisciplinarity: Lessons from the Scientific Response to COVID-19

  • 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.
Metadaten
Author:Eva SeidlmayerORCiD, Tetyana Melnychuk, Lukas Galke, Lisa Kühnel, Klaus Tochtermann, Carsten Schultz, Konrad U. Förstner
URN:urn:nbn:de:hbz:832-epub4-29007
DOI:https://doi.org/10.1007/s11192-024-05132-x
ISSN:0138-9130
ISSN:1588-2861
Parent Title (English):Scientometrics
Publisher:Springer International Publishing
Place of publication:Cham
Document Type:Article
Language:English
Date of Publication (online):2025/05/09
GND-Keyword:Bibliometrie; COVID-19; Interdisziplinarität; Maschinelles Lernen; Netzwerkanalyse
Tag:Research dynamics
Volume:129
Issue:9
Page Number:39
Institutes:Informations- und Kommunikationswissenschaften (F03) / Fakultät 03 / Institut für Informationswissenschaft
Dewey Decimal Classification:000 Allgemeines, Informatik, Informationswissenschaft
Open Access:Open Access
DeepGreen:DeepGreen
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International