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Aggregating different paper quality measures with a generalized h-index

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  • Gagolewski, Marek
  • Mesiar, Radko

Abstract

The process of assessing individual authors should rely upon a proper aggregation of reliable and valid papers’ quality metrics. Citations are merely one possible way to measure appreciation of publications. In this study we propose some new, SJR- and SNIP-based indicators, which not only take into account the broadly conceived popularity of a paper (manifested by the number of citations), but also other factors like its potential, or the quality of papers that cite a given publication. We explore the relation and correlation between different metrics and study how they affect the values of a real-valued generalized h-index calculated for 11 prominent scientometricians. We note that the h-index is a very unstable impact function, highly sensitive for applying input elements’ scaling. Our analysis is not only of theoretical significance: data scaling is often performed to normalize citations across disciplines. Uncontrolled application of this operation may lead to unfair and biased (toward some groups) decisions. This puts the validity of authors assessment and ranking using the h-index into question. Obviously, a good impact function to be used in practice should not be as much sensitive to changing input data as the analyzed one.

Suggested Citation

  • Gagolewski, Marek & Mesiar, Radko, 2012. "Aggregating different paper quality measures with a generalized h-index," Journal of Informetrics, Elsevier, vol. 6(4), pages 566-579.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:4:p:566-579
    DOI: 10.1016/j.joi.2012.05.001
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    References listed on IDEAS

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    Cited by:

    1. Cena, Anna & Gagolewski, Marek & Mesiar, Radko, 2015. "Problems and challenges of information resources producers’ clustering," Journal of Informetrics, Elsevier, vol. 9(2), pages 273-284.
    2. 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.
    3. James C. Ryan, 2016. "A validation of the individual annual h-index (hIa): application of the hIa to a qualitatively and quantitatively different sample," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 577-590, October.
    4. Gagolewski, Marek, 2013. "Scientific impact assessment cannot be fair," Journal of Informetrics, Elsevier, vol. 7(4), pages 792-802.
    5. Boczek, Michał & Hovana, Anton & Hutník, Ondrej & Kaluszka, Marek, 2021. "New monotone measure-based integrals inspired by scientific impact problem," European Journal of Operational Research, Elsevier, vol. 290(1), pages 346-357.
    6. Dašić Predrag, 2015. "State and Analysis of Scientific Journals in the Field of “Economic Sciences” for the Period 1995-2014," Economic Themes, Sciendo, vol. 53(4), pages 547-581, December.

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