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Universality of Citation Distributions for Academic Institutions and Journals

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  • Arnab Chatterjee
  • Asim Ghosh
  • Bikas K Chakrabarti

Abstract

Citations measure the importance of a publication, and may serve as a proxy for its popularity and quality of its contents. Here we study the distributions of citations to publications from individual academic institutions for a single year. The average number of citations have large variations between different institutions across the world, but the probability distributions of citations for individual institutions can be rescaled to a common form by scaling the citations by the average number of citations for that institution. We find this feature seems to be universal for a broad selection of institutions irrespective of the average number of citations per article. A similar analysis for citations to publications in a particular journal in a single year reveals similar results. We find high absolute inequality for both these sets, Gini coefficients being around 0.66 and 0.58 for institutions and journals respectively. We also find that the top 25% of the articles hold about 75% of the total citations for institutions and the top 29% of the articles hold about 71% of the total citations for journals.

Suggested Citation

  • Arnab Chatterjee & Asim Ghosh & Bikas K Chakrabarti, 2016. "Universality of Citation Distributions for Academic Institutions and Journals," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-11, January.
    • Handle: RePEc:plo:pone00:0146762
    DOI: 10.1371/journal.pone.0146762
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    1. Chakrabarti,Bikas K. & Chakraborti,Anirban & Chakravarty,Satya R. & Chatterjee,Arnab, 2013. "Econophysics of Income and Wealth Distributions," Cambridge Books, Cambridge University Press, number 9781107013445, December.
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    1. Delbianco, Fernando & Fioriti, Andrés & Hernandez-Chanto, Allan & Tohmé, Fernando, 2020. "A Markov-switching approach to the study of citations in academic journals," Journal of Informetrics, Elsevier, vol. 14(4).
    2. Fenghua Wang & Ying Fan & An Zeng & Zengru Di, 2019. "Can we predict ESI highly cited publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 109-125, January.
    3. 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.
    4. Cerqueti, Roy & Lupi, Claudio & Pietrovito, Filomena & Pozzolo, Alberto Franco, 2022. "Rank–size distributions for banks: A cross-country analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    5. R. Basurto-Flores & L. Guzmán-Vargas & S. Velasco & A. Medina & A. Calvo Hernandez, 2018. "On entropy research analysis: cross-disciplinary knowledge transfer," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 123-139, October.
    6. Mrowinski, Maciej J. & Gagolewski, Marek & Siudem, Grzegorz, 2022. "Accidentality in journal citation patterns," Journal of Informetrics, Elsevier, vol. 16(4).
    7. Zoltán Néda & Levente Varga & Tamás S Biró, 2017. "Science and Facebook: The same popularity law!," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-11, July.
    8. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Soldatenkova, Anastasiia, 2017. "An investigation on the skewness patterns and fractal nature of research productivity distributions at field and discipline level," Journal of Informetrics, Elsevier, vol. 11(1), pages 324-335.
    9. Chatterjee, Arnab & Ghosh, Asim & Chakrabarti, Bikas K., 2017. "Socio-economic inequality: Relationship between Gini and Kolkata indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 583-595.

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