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<p>In this paper, we investigate the properties of the science-wide author databases’ standardised citation indicators produced since 2015 by a team of researchers led by John P. A. Ioannidis. Based on data from Scopus, the researchers published close to 200,000 of the most-cited authors across all scientific fields and ranked them using a composite indicator that includes six citation metrics (total citations; Hirsch h-index; coauthorship-adjusted Schreiber hm-index; the number of citations to papers as a single author; the number of citations to papers as a single or first author; and the number of citations to papers as a single, first, or last author). We look at the quality of these new rankings to suggest improvements. Specifically, we try to correct for correlation among variables and the deviation between the purported weights of the variables, as declared by the developers, and the effective weights based on the theory of global sensitivity analysis. We aim to ascertain if our modified measure is an improvement over the existing one by addressing the issue of redundant data due to correlated variables. Lacking a ground truth based on a ‘true’ ranking possibly supported by independent data, we content ourselves for the purpose of the present analysis with benchmarking against a best practice. Given that most practitioners have reservations against linear aggregation approaches (Munda, Social multi-criteria evaluation for a sustainable economy, Springer, Berlin, 2008; Balinski &amp; Laraki, Majority judgment: Measuring, ranking, and electing, Mass, 2011; Arrow, Social choice and individual values, Martino Fine Books, Eastford, 2012 [1951]), while those based on the method of Condorcet are considered by many as a good practice (Munda, Social multi-criteria evaluation for a sustainable economy, Springer, Berlin, 2008; Roy, Multicriteria methodology for decision aiding, Springer, Cham, 1996), we entrust of defence of our modified ranking on a pairwise comparison between linear aggregation and Condorcet.</p>

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Ranking the rankers. An analysis of science-wide author databases of standardised citation indicators

  • 期刊论文

  • 发表日期：

    2025年2月18日

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  • Marta Kuc-Czarnecka,
  • Andrea Saltelli

摘要

In this paper, we investigate the properties of the science-wide author databases’ standardised citation indicators produced since 2015 by a team of researchers led by John P. A. Ioannidis. Based on data from Scopus, the researchers published close to 200,000 of the most-cited authors across all scientific fields and ranked them using a composite indicator that includes six citation metrics (total citations; Hirsch h-index; coauthorship-adjusted Schreiber hm-index; the number of citations to papers as a single author; the number of citations to papers as a single or first author; and the number of citations to papers as a single, first, or last author). We look at the quality of these new rankings to suggest improvements. Specifically, we try to correct for correlation among variables and the deviation between the purported weights of the variables, as declared by the developers, and the effective weights based on the theory of global sensitivity analysis. We aim to ascertain if our modified measure is an improvement over the existing one by addressing the issue of redundant data due to correlated variables. Lacking a ground truth based on a ‘true’ ranking possibly supported by independent data, we content ourselves for the purpose of the present analysis with benchmarking against a best practice. Given that most practitioners have reservations against linear aggregation approaches (Munda, Social multi-criteria evaluation for a sustainable economy, Springer, Berlin, 2008; Balinski & Laraki, Majority judgment: Measuring, ranking, and electing, Mass, 2011; Arrow, Social choice and individual values, Martino Fine Books, Eastford, 2012 [1951]), while those based on the method of Condorcet are considered by many as a good practice (Munda, Social multi-criteria evaluation for a sustainable economy, Springer, Berlin, 2008; Roy, Multicriteria methodology for decision aiding, Springer, Cham, 1996), we entrust of defence of our modified ranking on a pairwise comparison between linear aggregation and Condorcet.