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Toward a stochastically robust normalized impact factor against fraud and scams

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Abstract

In this paper, we model the variation of the bibliometric measure differences across academic fields in order to quantify the sources of these discrepancies. Since the bibliometric measure is based on the amount of published and cited papers, we anticipate that the mean number of references by published paper is the predominant parameter behind the discrepancies of impact factor scores in some academic fields. We introduce here a bias-free model, based on normalized variables with restricted cross-discipline discrepancies, that is robust against fraud and scams. The model is then submitted to an intensive numerical test using a Monte Carlo simulation.

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Authors and Affiliations

  1. Mediterranean School of Business, South Mediterranean University, Les Jardins du Lac 2, 1053, Tunis, Tunisia

    Khaled Belkadhi

  2. Unité de Recherche de Physique Nucléaire et des Hautes Énergies, Faculté des Sciences de Tunis, Université Tunis El Manar, Tunis, Tunisia

    Khaled Belkadhi & Adel Trabelsi

  3. Centre National de Sciences et de Technologies Nucléaires, Sidi Thabet, Tunisia

    Adel Trabelsi

Authors
  1. Khaled Belkadhi
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  2. Adel Trabelsi
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Correspondence to Khaled Belkadhi.

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Belkadhi, K., Trabelsi, A. Toward a stochastically robust normalized impact factor against fraud and scams. Scientometrics 124, 1871–1884 (2020). https://doi.org/10.1007/s11192-020-03577-4

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  • DOI: https://doi.org/10.1007/s11192-020-03577-4

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