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Analyzing journal category assignment using a paper-level classification system: multidisciplinary sciences journals

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  • Jiandong Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zhesi Shen

    (Chinese Academy of Sciences)

Abstract

Accurate identification and classification of “multidisciplinary” journals is crucial in scientometrics for revealing scientific structure and evaluating journals. Using Web of Science data from 2016 to 2020, we calculated disciplinary diversity of 12,225 journals based on paper-level subject classifications. We conducted a systematic analysis comparing Journal Citation Reports (JCR) multidisciplinary journals versus non-multidisciplinary journals. Results showed most JCR multidisciplinary journals have high disciplinary diversity, while non-multidisciplinary journals tend to have lower diversity. Some multidisciplinary journals with low diversity may be misclassified. We also found inconsistencies in journal disciplinary diversity at three granularity levels of the classification system. Visual analysis identified four distribution types of multidisciplinary journal diversity. Furthermore, ten potential multidisciplinary journals were identified in non-multidisciplinary JCR categories. Analysis of these journals showed two distinct publishing behaviors—some continuously publish across multiple fields, while others constantly change focus between fields.

Suggested Citation

  • Jiandong Zhang & Zhesi Shen, 2024. "Analyzing journal category assignment using a paper-level classification system: multidisciplinary sciences journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(10), pages 5963-5978, October.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:10:d:10.1007_s11192-023-04913-0
    DOI: 10.1007/s11192-023-04913-0
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    References listed on IDEAS

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