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Statistical validation of a global model for the distribution of the ultimate number of citations accrued by papers published in a scientific journal

Author

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  • Michael J. Stringer
  • Marta Sales‐Pardo
  • Luís A. Nunes Amaral

Abstract

A central issue in evaluative bibliometrics is the characterization of the citation distribution of papers in the scientific literature. Here, we perform a large‐scale empirical analysis of journals from every field in Thomson Reuters' Web of Science database. We find that only 30 of the 2,184 journals have citation distributions that are inconsistent with a discrete lognormal distribution at the rejection threshold that controls the false discovery rate at 0.05. We find that large, multidisciplinary journals are over‐represented in this set of 30 journals, leading us to conclude that, within a discipline, citation distributions are lognormal. Our results strongly suggest that the discrete lognormal distribution is a globally accurate model for the distribution of “eventual impact” of scientific papers published in single‐discipline journal in a single year that is removed sufficiently from the present date.

Suggested Citation

  • Michael J. Stringer & Marta Sales‐Pardo & Luís A. Nunes Amaral, 2010. "Statistical validation of a global model for the distribution of the ultimate number of citations accrued by papers published in a scientific journal," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(7), pages 1377-1385, July.
    • Handle: RePEc:bla:jamist:v:61:y:2010:i:7:p:1377-1385
    DOI: 10.1002/asi.21335
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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.
    2. Daniele Rotolo & Michael Hopkins & Nicola Grassano, 2023. "Do funding sources complement or substitute? Examining the impact of cancer research publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 50-66, January.
    3. Brito, Ricardo & Navarro, Alonso Rodríguez, 2021. "The inconsistency of h-index: A mathematical analysis," Journal of Informetrics, Elsevier, vol. 15(1).
    4. David I Stern, 2014. "High-Ranked Social Science Journal Articles Can Be Identified from Early Citation Information," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-11, November.
    5. José M Miotto & Eduardo G Altmann, 2014. "Predictability of Extreme Events in Social Media," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-7, November.
    6. João A G Moreira & Xiao Han T Zeng & Luís A Nunes Amaral, 2015. "The Distribution of the Asymptotic Number of Citations to Sets of Publications by a Researcher or from an Academic Department Are Consistent with a Discrete Lognormal Model," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-17, November.
    7. Jiahang Lyu & Saralees Nadarajah, 2022. "Discrete lognormal distributions with application to insurance data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1268-1282, June.
    8. Keye Wu & Ziyue Xie & Jia Tina Du, 2024. "Does science disrupt technology? Examining science intensity, novelty, and recency through patent-paper citations in the pharmaceutical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5469-5491, September.
    9. Koon-Kiu Yan & Mark Gerstein, 2011. "The Spread of Scientific Information: Insights from the Web Usage Statistics in PLoS Article-Level Metrics," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-7, May.
    10. Oliveira, Diego F.M. & Chan, Kevin S., 2019. "The effects of trust and influence on the spreading of low and high quality information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 657-663.
    11. 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.
    12. Joshua Fischman, 2024. "A statistical approach to law school citation rankings," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 21(3), pages 632-668, September.
    13. 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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