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The accuracy of field classifications for journals in Scopus

Author

Listed:
  • Mike Thelwall

    (University of Sheffield)

  • Stephen Pinfield

    (University of Sheffield)

Abstract

Journal field classifications in Scopus are used for citation-based indicators and by authors choosing appropriate journals to submit to. Whilst prior research has found that Scopus categories are occasionally misleading, it is not known how this varies for different journal types. In response, we assessed whether specialist, cross-field and general academic journals sometimes have publication practices that do not match their Scopus classifications. For this, we compared the Scopus narrow fields of journals with the fields that best fit their articles’ titles and abstracts. We also conducted qualitative follow-up to distinguish between Scopus classification errors and misleading journal aims. The results show sharp field differences in the extent to which both cross-field and apparently specialist journals publish articles that match their Scopus narrow fields, and the same for general journals. The results also suggest that a few journals have titles and aims that do not match their contents well, and that some large topics spread themselves across many relevant fields. Thus, the likelihood that a journal’s Scopus narrow fields reflect its contents varies substantially by field (although without systematic field trends) and some cross-field topics seem to cause difficulties in appropriately classifying relevant journals. These issues undermine citation-based indicators that rely on journal-level classification and may confuse scholars seeking publishing venues.

Suggested Citation

  • Mike Thelwall & Stephen Pinfield, 2024. "The accuracy of field classifications for journals in Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(2), pages 1097-1117, February.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:2:d:10.1007_s11192-023-04901-4
    DOI: 10.1007/s11192-023-04901-4
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    References listed on IDEAS

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    1. Wang, Qi & Waltman, Ludo, 2016. "Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus," Journal of Informetrics, Elsevier, vol. 10(2), pages 347-364.
    2. Richard Klavans & Kevin W. Boyack, 2017. "Which Type of Citation Analysis Generates the Most Accurate Taxonomy of Scientific and Technical Knowledge?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 984-998, April.
    3. Antonio J. Gómez-Núñez & Benjamín Vargas-Quesada & Félix Moya-Anegón, 2016. "Updating the SCImago journal and country rank classification: A new approach using Ward's clustering and alternative combination of citation measures," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(1), pages 178-190, January.
    4. Alberto Martín-Martín & Mike Thelwall & Enrique Orduna-Malea & Emilio Delgado López-Cózar, 2021. "Correction to: Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 907-908, January.
    5. repec:plo:pone00:0039464 is not listed on IDEAS
    6. Jing Zhang & Xiaomin Liu & Lili Wu, 2016. "The study of subject-classification based on journal coupling and expert subject-classification system," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1149-1170, June.
    7. Kevin W Boyack & David Newman & Russell J Duhon & Richard Klavans & Michael Patek & Joseph R Biberstine & Bob Schijvenaars & André Skupin & Nianli Ma & Katy Börner, 2011. "Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    8. Alberto Martín-Martín & Mike Thelwall & Enrique Orduna-Malea & Emilio Delgado López-Cózar, 2021. "Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 871-906, January.
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