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How to derive an advantage from the arbitrariness of the g-index

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  • Schreiber, Michael

Abstract

The definition of the g-index is as arbitrary as that of the h-index, because the threshold number g2 of citations to the g most cited papers can be modified by a prefactor at one's discretion, thus taking into account more or less of the highly cited publications within a dataset. In a case study I investigate the citation records of 26 physicists and show that the prefactor influences the ranking in terms of the generalized g-index less than for the generalized h-index. I propose specifically a prefactor of 2 for the g-index, because then the resulting values are of the same order of magnitude as for the common h-index. In this way one can avoid the disadvantage of the original g-index, namely that the values are usually substantially larger than for the h-index and thus the precision problem is substantially larger; while the advantages of the g-index over the h-index are kept. Like for the generalized h-index, also for the generalized g-index different prefactors might be more useful for investigations which concentrate only on top scientists with high citation frequencies or on junior researchers with small numbers of citations.

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  • Schreiber, Michael, 2013. "How to derive an advantage from the arbitrariness of the g-index," Journal of Informetrics, Elsevier, vol. 7(2), pages 555-561.
    • Handle: RePEc:eee:infome:v:7:y:2013:i:2:p:555-561
    DOI: 10.1016/j.joi.2013.02.003
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    References listed on IDEAS

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    1. Schreiber, Michael, 2013. "A case study of the arbitrariness of the h-index and the highly-cited-publications indicator," Journal of Informetrics, Elsevier, vol. 7(2), pages 379-387.
    2. Michael Schreiber, 2010. "Revisiting the g-index: The average number of citations in the g-core," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(1), pages 169-174, January.
    3. Glenn Ellison, 2013. "How Does the Market Use Citation Data? The Hirsch Index in Economics," American Economic Journal: Applied Economics, American Economic Association, vol. 5(3), pages 63-90, July.
    4. van Eck, Nees Jan & Waltman, Ludo, 2008. "Generalizing the h- and g-indices," Journal of Informetrics, Elsevier, vol. 2(4), pages 263-271.
    5. Sune Lehmann & Andrew D. Jackson & Benny E. Lautrup, 2006. "Measures for measures," Nature, Nature, vol. 444(7122), pages 1003-1004, December.
    6. van Eck, N.J.P. & Waltman, L., 2008. "Generalizing the h- and g-indices," ERIM Report Series Research in Management ERS-2008-049-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Sune Lehmann & Andrew D. Jackson & Benny E. Lautrup, 2008. "A quantitative analysis of indicators of scientific performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(2), pages 369-390, August.
    8. Martin Ravallion & Adam Wagstaff, 2011. "On measuring scholarly influence by citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 321-337, July.
    9. Michael Schreiber, 2008. "An empirical investigation of the g‐index for 26 physicists in comparison with the h‐index, the A‐index, and the R‐index," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(9), pages 1513-1522, July.
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    3. Fabio Zagonari, 2019. "Scientific Production and Productivity for Characterizing an Author’s Publication History: Simple and Nested Gini’s and Hirsch’s Indexes Combined," Publications, MDPI, vol. 7(2), pages 1-30, May.

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