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Keyword occurrences and journal specialization

Author

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  • Gabriele Sampagnaro

    (University of Naples Parthenope)

Abstract

Since the borders of disciplines change over time and vary across communities and geographies, they can be expressed at different levels of granularity, making it challenging to find a broad consensus about the measurement of interdisciplinarity. This study contributes to this debate by proposing a journal specialization index based on the level of repetitiveness of keywords appearing in their articles. Keywords represent one of the most essential items for filtering the vast amount of research available. If chosen correctly, they can help to identify the central concept of the paper and, consequently, to couple it with manuscripts related to the same field or subfield of research. Based on these universally recognized features of article keywords, the study proposes measuring the specialization of a journal by counting the number of times that a keyword is Queryrepeated in a journal on average (Sj). The basic assumption underlying the proposal of a journal specialization index is that the keywords may approximate the article’s topic and that the higher the number of papers in a journal based on a topic, the higher the level of specialization of that journal. The proposed specialization metric is not invulnerable to a set of limitations, among which the most relevant seems to be the lack of a standard practice regarding the number and consistency of keywords appearing in each article.

Suggested Citation

  • Gabriele Sampagnaro, 2023. "Keyword occurrences and journal specialization," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5629-5645, October.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:10:d:10.1007_s11192-023-04815-1
    DOI: 10.1007/s11192-023-04815-1
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    References listed on IDEAS

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    1. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    2. Jian Xu & Yi Bu & Ying Ding & Sinan Yang & Hongli Zhang & Chen Yu & Lin Sun, 2018. "Understanding the formation of interdisciplinary research from the perspective of keyword evolution: a case study on joint attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 973-995, November.
    3. Uddin, Shahadat & Khan, Arif, 2016. "The impact of author-selected keywords on citation counts," Journal of Informetrics, Elsevier, vol. 10(4), pages 1166-1177.
    4. Leydesdorff, Loet & Rafols, Ismael, 2011. "Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations," Journal of Informetrics, Elsevier, vol. 5(1), pages 87-100.
    5. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    6. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    7. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    8. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Zhang, Lin, 2018. "A comparison of two approaches for measuring interdisciplinary research output: The disciplinary diversity of authors vs the disciplinary diversity of the reference list," Journal of Informetrics, Elsevier, vol. 12(4), pages 1182-1193.
    9. Loet Leydesdorff, 2007. "Mapping interdisciplinarity at the interfaces between the Science Citation Index and the Social Science Citation Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 71(3), pages 391-405, June.
    10. Mark P. Carpenter & Francis Narin, 1973. "Clustering of scientific journals," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(6), pages 425-436, November.
    11. Sander Zwanenburg & Maryam Nakhoda & Peter Whigham, 2022. "Toward greater consistency and validity in measuring interdisciplinarity: a systematic and conceptual evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7769-7788, December.
    12. Ludo Waltman & Nees Jan van Eck, 2012. "A new methodology for constructing a publication‐level classification system of science," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    13. Bo Jarneving, 2001. "The cognitive structure of current cardiovascular research," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 365-389, March.
    14. Wolfgang Glänzel & Koenraad Debackere, 2022. "Various aspects of interdisciplinarity in research and how to quantify and measure those," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5551-5569, September.
    15. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    16. Munan Li, 2018. "Classifying and ranking topic terms based on a novel approach: role differentiation of author keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 77-100, July.
    17. Shu, Fei & Julien, Charles-Antoine & Zhang, Lin & Qiu, Junping & Zhang, Jing & Larivière, Vincent, 2019. "Comparing journal and paper level classifications of science," Journal of Informetrics, Elsevier, vol. 13(1), pages 202-225.
    18. W. Glänzel & A. Schubert & H. -J. Czerwon, 1999. "An item-by-item subject classification of papers published in multidisciplinary and general journals using reference analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 44(3), pages 427-439, March.
    19. Fei Shu & Yue Ma & Junping Qiu & Vincent Larivière, 2020. "Classifications of science and their effects on bibliometric evaluations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2727-2744, December.
    20. Franceschini, Fiorenzo & Maisano, Domenico & Mastrogiacomo, Luca, 2016. "Empirical analysis and classification of database errors in Scopus and Web of Science," Journal of Informetrics, Elsevier, vol. 10(4), pages 933-953.
    21. Xin Gu & Karen L. Blackmore, 2016. "Recent trends in academic journal growth," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 693-716, August.
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