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Measuring the disparity among scientific disciplines using Library of Congress Subject Headings

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Abstract

Examining the relationships among scientific disciplines is important today, but existing methods are limited by the contents and structure of their bibliographic databases. We therefore demonstrate a novel approach that measures disparity by examining the organization of published scientific books and monographs into Library of Congress Subject Headings. After outlining the method and analyses conducted, we compare our results with those produced by prior works, note potential implications of the demonstrated method for use by bibliometric practitioners, and suggest directions for further research.

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Notes

  1. There are 1,065 books published before 1830 including 1,018 books in which publishing year information is missing.

  2. Cosine similarity has been frequently used to measure the disparity (or similarity) among disciplines in previous bibliometric studies (Ahlgren et al., 2003; Huang et al., 2021; Zhang et al., 2016; Zhang et al., 2021). It provides more reasonable and intuitive results for measuring the similarity among scientific disciplines compared with the measurement by Person correlation (Klavans & Boyack, 2006).

  3. NSF classification system is a 2-level journal classification system consisting of 14 major disciplines and 144 subfields. This system exclusively assigns each individual journal into only one single field. In this study, the disparity measure was calculated at the level of 14 major disciplines in SSH (i.e., Arts, Health, Humanities, Professional fields, Psychology, and Social sciences) and NSE (i.e., Biology, Chemistry, Engineering and Technology, Mathematics, Clinical medicine, Physics, Biomedical Research, and Earth and space science).

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Acknowledgements

This work is supported by Zhejiang Provincial Philosophy and Social Sciences Planning Project (22NDJC085YB). We also would like to thank Philippe Mongeon for his data analysis assistance and his helpful comments.

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Authors and Affiliations

  1. Chinese Academy of Science and Education Evaluation (CASSE), Hangzhou Dianzi University, Hangzhou, Zhejiang, People’s Republic of China

    Fei Shu & Shiji Chen

  2. École de Bibliothéconomie et Des Sciences de L’information, Université de Montréal, Montréal, Québec, Canada

    Fei Shu

  3. Berlin School of Library and Information Science, Humboldt-Universität Zu Berlin, Berlin, Germany

    Jesse David Dinneen

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  1. Fei Shu
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Correspondence to Fei Shu.

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Shu, F., Dinneen, J.D. & Chen, S. Measuring the disparity among scientific disciplines using Library of Congress Subject Headings. Scientometrics 127, 3613–3628 (2022). https://doi.org/10.1007/s11192-022-04387-6

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