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Universality of citation distributions revisited

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  • Ludo Waltman
  • Nees Jan van Eck
  • Anthony F. J. van Raan

Abstract

Radicchi, Fortunato, and Castellano (2008) claim that, apart from a scaling factor, all fields of science are characterized by the same citation distribution. We present a large‐scale validation study of this universality‐of‐citation‐distributions claim. Our analysis shows that claiming citation distributions to be universal for all fields of science is not warranted. Although many fields indeed seem to have fairly similar citation distributions, there are exceptions as well. We also briefly discuss the consequences of our findings for the measurement of scientific impact using citation‐based bibliometric indicators.

Suggested Citation

  • Ludo Waltman & Nees Jan van Eck & Anthony F. J. van Raan, 2012. "Universality of citation distributions revisited," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 72-77, January.
    • Handle: RePEc:bla:jamist:v:63:y:2012:i:1:p:72-77
    DOI: 10.1002/asi.21671
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    Cited by:

    1. Weilong Bi & Ho Fai Chan & Benno Torgler, 2019. "Self-esteem, self-symbolizing, and academic recognition: behavioral evidence from curricula vitae," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 495-525, April.
    2. Ruiz-Castillo, Javier & Costas, Rodrigo, 2014. "The skewness of scientific productivity," Journal of Informetrics, Elsevier, vol. 8(4), pages 917-934.
    3. Giancarlo Ruocco & Cinzia Daraio, 2013. "An empirical approach to compare the performance of heterogeneous academic fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 601-625, December.
    4. Ruiz-Castillo, Javier & Costas, Rodrigo, 2018. "Individual and field citation distributions in 29 broad scientific fields," Journal of Informetrics, Elsevier, vol. 12(3), pages 868-892.
    5. Loet Leydesdorff & Ping Zhou & Lutz Bornmann, 2013. "How can journal impact factors be normalized across fields of science? An assessment in terms of percentile ranks and fractional counts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 96-107, January.
    6. Ruiz-Castillo, Javier & Waltman, Ludo, 2015. "Field-normalized citation impact indicators using algorithmically constructed classification systems of science," Journal of Informetrics, Elsevier, vol. 9(1), pages 102-117.
    7. S. R. Goldberg & H. Anthony & T. S. Evans, 2015. "Modelling citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1577-1604, December.
    8. Thelwall, Mike & Wilson, Paul, 2014. "Distributions for cited articles from individual subjects and years," Journal of Informetrics, Elsevier, vol. 8(4), pages 824-839.
    9. Javier Ruiz-Castillo, 2013. "The role of statistics in establishing the similarity of citation distributions in a static and a dynamic context," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 173-181, July.
    10. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2012. "How important is choice of the scaling factor in standardizing citations?," Journal of Informetrics, Elsevier, vol. 6(4), pages 645-654.
    11. Zhihui Zhang & Ying Cheng & Nian Cai Liu, 2015. "Improving the normalization effect of mean-based method from the perspective of optimization: optimization-based linear methods and their performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 587-607, January.
    12. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "On the interplay between normalisation, bias, and performance of paper impact metrics," Journal of Informetrics, Elsevier, vol. 13(1), pages 270-290.
    13. Thelwall, Mike, 2016. "Are the discretised lognormal and hooked power law distributions plausible for citation data?," Journal of Informetrics, Elsevier, vol. 10(2), pages 454-470.
    14. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    15. Zhihui Zhang & Ying Cheng & Nian Cai Liu, 2014. "Comparison of the effect of mean-based method and z-score for field normalization of citations at the level of Web of Science subject categories," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 1679-1693, December.
    16. You, Taekho & Park, Jinseo & Lee, June Young & Yun, Jinhyuk & Jung, Woo-Sung, 2022. "Disturbance of questionable publishing to academia," Journal of Informetrics, Elsevier, vol. 16(2).
    17. Bouyssou, Denis & Marchant, Thierry, 2016. "Ranking authors using fractional counting of citations: An axiomatic approach," Journal of Informetrics, Elsevier, vol. 10(1), pages 183-199.
    18. T. S. Evans & N. Hopkins & B. S. Kaube, 2012. "Universality of performance indicators based on citation and reference counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 473-495, November.
    19. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.
    20. Wang, Xing & Zhang, Zhihui, 2020. "Improving the reliability of short-term citation impact indicators by taking into account the correlation between short- and long-term citation impact," Journal of Informetrics, Elsevier, vol. 14(2).
    21. Vaccario, Giacomo & Medo, Matúš & Wider, Nicolas & Mariani, Manuel Sebastian, 2017. "Quantifying and suppressing ranking bias in a large citation network," Journal of Informetrics, Elsevier, vol. 11(3), pages 766-782.
    22. Andrea Bonaccorsi & Cinzia Daraio & Stefano Fantoni & Viola Folli & Marco Leonetti & Giancarlo Ruocco, 2017. "Do social sciences and humanities behave like life and hard sciences?," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 607-653, July.

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