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University citation distributions

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  • Antonio Perianes-Rodriguez
  • Javier Ruiz-Castillo

Abstract

In this paper we investigate the characteristics of the citation distributions of the 500 universities inthe 2013 edition of the CWTS Leiden Ranking. We use a WoS dataset consisting of 3.6 million articles published in 2003-2008 with a five-year citation window, and classified into 5,119 clusters. The mainfindings are the following four. Firstly, The universality claim, according to which all university citation distributions, appropriately normalized, follow a single functional form, is not supported by the data. Secondly, nevertheless, the 500 university citation distributions are all highly skewed and very similar.Broadly speaking, university citation distributions appear to behave as if they differ by a relatively constant scale factor over a large, intermediate part of their support. Thirdly, citation impact differences between universities account for 3.85% of overall citation inequality. However, these differences are greatly reduced when university citation distributions are normalized using their MNCS values as normalization factors. Finally, the above results have important practical consequences. On one hand, we only need a single explanatory model for the single type of high skewness characterizing all university citation distributions. On the other hand, the similarity of university citation distributions goes a long way in explaining the similarity of the university rankings obtained with the MNCS and the top 10% indicator.
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Suggested Citation

  • Antonio Perianes-Rodriguez & Javier Ruiz-Castillo, 2016. "University citation distributions," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(11), pages 2790-2804, November.
    • Handle: RePEc:bla:jinfst:v:67:y:2016:i:11:p:2790-2804
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    File URL: http://hdl.handle.net/10.1002/asi.23619
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    Cited by:

    1. 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.
    2. Giovanni Abramo & Ciriaco Andrea D’Angelo & Anastasiia Soldatenkova, 2016. "The dispersion of the citation distribution of top scientists’ publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1711-1724, December.
    3. Vîiu, Gabriel-Alexandru, 2017. "Disaggregated research evaluation through median-based characteristic scores and scales: a comparison with the mean-based approach," Journal of Informetrics, Elsevier, vol. 11(3), pages 748-765.
    4. Antonio Perianes-Rodriguez & Javier Ruiz-Castillo, 2016. "A comparison of two ways of evaluating research units working in different scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 539-561, February.
    5. 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.

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