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The lognormal distribution explains the remarkable pattern documented by characteristic scores and scales in scientometrics

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  • Vîiu, Gabriel-Alexandru

Abstract

Characteristic scores and scales (CSS) – a well-established scientometric tool for the study of citation counts – have been used to document a striking phenomenon that characterizes citation distributions at high levels of aggregation: irrespective of scientific field and citation window empirical studies find a persistent pattern whereby about 70% of scientific papers belong to the class of poorly cited papers, about 21% belong to the class of fairly cited papers, 6% to that of remarkably cited papers and 3% to the class of outstandingly cited papers. This article aims to advance the understanding of this remarkable result by examining it in the context of the lognormal distribution, a popular model used to describe citation counts across scientific fields. The article shows that the application of the CSS method to lognormal distributions provides a very good fit to the 70–21–6–3% empirical pattern provided these distributions are characterized by a standard deviation parameter in the range of about 0.8–1.3. The CSS pattern is essentially explainable as an epiphenomenon of the lognormal functional form and, more generally, as a consequence of the skewness of science which is manifest in heavy-tailed citation distributions.

Suggested Citation

  • Vîiu, Gabriel-Alexandru, 2018. "The lognormal distribution explains the remarkable pattern documented by characteristic scores and scales in scientometrics," Journal of Informetrics, Elsevier, vol. 12(2), pages 401-415.
  • Handle: RePEc:eee:infome:v:12:y:2018:i:2:p:401-415
    DOI: 10.1016/j.joi.2018.02.002
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    References listed on IDEAS

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    Cited by:

    1. Alonso Rodríguez-Navarro & Ricardo Brito, 2019. "Probability and expected frequency of breakthroughs: basis and use of a robust method of research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 213-235, April.

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