Exploring the interdisciplinarity patterns of highly cited papers.
Chen, S., Qiu, J., Arsenault, C. et Larivière, V. (2021). Exploring the interdisciplinarity patterns of highly cited papers. Journal of Informetrics, 15(1).
Chen, S., Qiu, J., Arsenault, C. et Larivière, V. (2021). Exploring the interdisciplinarity patterns of highly cited papers. Journal of Informetrics, 15(1).
This study explores the relationship between interdisciplinarity and the citation impact of highly cited papers. In this paper, interdisciplinarity is investigated by comparing the dimensions of disciplinary diversity (variety, balance, and disparity) and the typical integration indicators (i.e., the Rao-Stirling index (RS) and the Leinster–Cobbold Diversity Index (LCDiv)) of all papers published in 2000 and indexed in Clarivate Analytics’ Web of Science. These papers are categorized into six percentile rank classes according to their citation rates, and the interdisciplinarity among these percentile rank classes is compared. Our results demonstrate that, whether control variables are considered or not, highly cited papers always exhibit higher variety and disparity, but they also exhibit lower balance. In terms of the integration interdisciplinarity indicators, the RS and LCDiv both have a positive effect on citation impact. From the perspective of effect size, our results suggest that the effect of variety on citation impact is most significant, followed by disparity and then balance. These results indicate that variety is likely the most important interdisciplinary factor for citation impact.
Ce contenu a été mis à jour le 17 novembre 2021 à 11 h 48 min.