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The extreme upper tail of Japan’s citation distribution reveals its research success

Author

Listed:
  • Alonso Rodríguez-Navarro

    (Universidad Politécnica de Madrid
    Universidad Complutense de Madrid)

  • Ricardo Brito

    (Universidad Complutense de Madrid)

Abstract

A number of indications, such as the number of Nobel Prize winners, show Japan to be a scientifically advanced country. However, standard bibliometric indicators place Japan as a scientifically developing country. The present study is based on the conjecture that Japan is an extreme case of a general pattern in highly industrialized countries. In these countries, scientific publications come from two types of studies: some pursue the advancement of science and produce highly cited publications, while others pursue incremental progress and their publications have a very low probability of being highly cited. Although these two categories of papers cannot be easily identified and separated, the scientific level of Japan can be tested by studying the extreme upper tail of the citation distribution of all scientific articles. In contrast to standard bibliometric indicators, which are calculated from the total number of papers or from sets of papers in which the two categories of papers are mixed, in the extreme upper tail, only papers that are addressed to the advance of science will be present. Based on the extreme upper tail, Japan belongs to the group of scientifically advanced countries and is significantly different from countries with a low scientific level. The number of Clarivate Citation laureates also supports our hypothesis that some citation-based metrics do not reveal the high scientific level of Japan. Our findings suggest that Japan is an extreme case of inaccuracy of some citation metrics; the same drawback might affect other countries, although to a lesser degree.

Suggested Citation

  • Alonso Rodríguez-Navarro & Ricardo Brito, 2024. "The extreme upper tail of Japan’s citation distribution reveals its research success," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3831-3844, August.
  • Handle: RePEc:spr:qualqt:v:58:y:2024:i:4:d:10.1007_s11135-024-01837-6
    DOI: 10.1007/s11135-024-01837-6
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    References listed on IDEAS

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    1. Sonia R. Zanotto & Cristina Haeffner & Jorge A. Guimarães, 2016. "Unbalanced international collaboration affects adversely the usefulness of countries’ scientific output as well as their technological and social impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1789-1814, December.
    2. Miranda, Ruben & Garcia-Carpintero, Esther, 2018. "Overcitation and overrepresentation of review papers in the most cited papers," Journal of Informetrics, Elsevier, vol. 12(4), pages 1015-1030.
    3. Ludo Waltman & Nees Jan van Eck & Anthony F. J. van Raan, 2012. "Universality of citation distributions revisited," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 72-77, January.
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