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Universality of performance indicators based on citation and reference counts

Author

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  • T. S. Evans

    (Imperial College)

  • N. Hopkins

    (Imperial College)

  • B. S. Kaube

    (Imperial College)

Abstract

We find evidence for the universality of two relative bibliometric indicators of the quality of individual scientific publications taken from different data sets. One of these is a new index that considers both citation and reference counts. We demonstrate this universality for relatively well cited publications from a single institute, grouped by year of publication and by faculty or by department. We show similar behaviour in publications submitted to the arXiv e-print archive, grouped by year of submission and by sub-archive. We also find that for reasonably well cited papers this distribution is well fitted by a lognormal with a variance of around σ2 = 1.3 which is consistent with the results of Radicchi et al. (Proc Natl Acad Sci USA 105:17268–17272, 2008). Our work demonstrates that comparisons can be made between publications from different disciplines and publication dates, regardless of their citation count and without expensive access to the whole world-wide citation graph. Further, it shows that averages of the logarithm of such relative bibliometric indices deal with the issue of long tails and avoid the need for statistics based on lengthy ranking procedures.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:scient:v:93:y:2012:i:2:d:10.1007_s11192-012-0694-9
    DOI: 10.1007/s11192-012-0694-9
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    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. Thelwall, Mike, 2016. "Citation count distributions for large monodisciplinary journals," Journal of Informetrics, Elsevier, vol. 10(3), pages 863-874.
    5. Brito, Ricardo & Navarro, Alonso Rodríguez, 2021. "The inconsistency of h-index: A mathematical analysis," Journal of Informetrics, Elsevier, vol. 15(1).
    6. Yanzhu Hu & Huiyang Zhao & Xinbo Ai, 2016. "Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-25, November.
    7. Rodríguez-Navarro, Alonso & Brito, Ricardo, 2018. "Double rank analysis for research assessment," Journal of Informetrics, Elsevier, vol. 12(1), pages 31-41.
    8. S. R. Goldberg & H. Anthony & T. S. Evans, 2015. "Modelling citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1577-1604, December.
    9. 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.
    10. Thelwall, Mike, 2016. "The precision of the arithmetic mean, geometric mean and percentiles for citation data: An experimental simulation modelling approach," Journal of Informetrics, Elsevier, vol. 10(1), pages 110-123.
    11. Campanario, Juan Miguel, 2015. "Providing impact: The distribution of JCR journals according to references they contribute to the 2-year and 5-year journal impact factors," Journal of Informetrics, Elsevier, vol. 9(2), pages 398-407.
    12. Thelwall, Mike & Sud, Pardeep, 2016. "National, disciplinary and temporal variations in the extent to which articles with more authors have more impact: Evidence from a geometric field normalised citation indicator," Journal of Informetrics, Elsevier, vol. 10(1), pages 48-61.
    13. Tanya Araújo & Elsa Fontainha, 2018. "Are scientific memes inherited differently from gendered authorship?," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 953-972, November.
    14. Xie, Zheng & Ouyang, Zhenzheng & Liu, Qi & Li, Jianping, 2016. "A geometric graph model for citation networks of exponentially growing scientific papers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 167-175.
    15. 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.
    16. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2018. "Research assessment by percentile-based double rank analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 315-329.
    17. Thelwall, Mike, 2016. "The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression," Journal of Informetrics, Elsevier, vol. 10(2), pages 336-346.
    18. Clough, James R. & Evans, Tim S., 2016. "What is the dimension of citation space?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 235-247.
    19. Thelwall, Mike & Wilson, Paul, 2014. "Regression for citation data: An evaluation of different methods," Journal of Informetrics, Elsevier, vol. 8(4), pages 963-971.
    20. Cinzia Daraio & Giancarlo Ruocco, 2012. "An Empirical Approach to Compare the Performance of Heterogeneous Academic Fields," DIAG Technical Reports 2012-03, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    21. 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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