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High-end performance or outlier? Evaluating the tail of scientometric distributions

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  • Wolfgang Glänzel

    (Centre for R&D Monitoring and Department of MSI, KU Leuven
    Library of the Hungarian Academy of Sciences)

Abstract

The present paper attempts to shed light on outstanding research performance using the example of citation distributions. In order to answer the question of how the analysis of outstanding performance, in general, and highly cited papers, in particular, could be integrated into standard techniques of evaluative scientometrics. Two general methods are proposed: One solution aims at quantifying the performance represented by the tail of citation distributions independently of the “mainstream”, the second one, a parameter-free solution, provides performance classes for any level. Advantages and shortcoming of both methods are discussed.

Suggested Citation

  • Wolfgang Glänzel, 2013. "High-end performance or outlier? Evaluating the tail of scientometric distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(1), pages 13-23, October.
  • Handle: RePEc:spr:scient:v:97:y:2013:i:1:d:10.1007_s11192-013-1022-8
    DOI: 10.1007/s11192-013-1022-8
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    References listed on IDEAS

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    1. Loet Leydesdorff & Lutz Bornmann & Rüdiger Mutz & Tobias Opthof, 2011. "Turning the tables on citation analysis one more time: Principles for comparing sets of documents," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1370-1381, July.
    2. Wolfgang Glänzel & Henk F. Moed, 2013. "Opinion paper: thoughts and facts on bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 381-394, July.
    3. Beirlant, Jan & Glänzel, Wolfgang & Carbonez, An & Leemans, Herlinde, 2007. "Scoring research output using statistical quantile plotting," Journal of Informetrics, Elsevier, vol. 1(3), pages 185-192.
    4. Matthys, Gunther & Delafosse, Emmanuel & Guillou, Armelle & Beirlant, Jan, 2004. "Estimating catastrophic quantile levels for heavy-tailed distributions," Insurance: Mathematics and Economics, Elsevier, vol. 34(3), pages 517-537, June.
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    Cited by:

    1. Guoliang Lyu & Ganwei Shi, 2019. "On an approach to boosting a journal’s citation potential," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1387-1409, September.
    2. Qing Cheng & Xin Lu & Zhong Liu & Jincai Huang, 2015. "Mining research trends with anomaly detection models: the case of social computing research," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 453-469, May.
    3. Campanario, Juan Miguel, 2014. "Analysis of the distribution of cited journals according to their positions in the h-core of citing journal listed in Journal Citation Reports," Journal of Informetrics, Elsevier, vol. 8(3), pages 534-545.

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