Abstract

We demonstrate that by using a triple of simple numerical summaries: an author’s productivity, their overall impact, and a single other bibliometric index that aims to capture the shape of the citation distribution, we can reconstruct other popular metrics of bibliometric impact with a sufficient degree of precision. We thus conclude that the use of many indices may be unnecessary – entities should not be multiplied beyond necessity. Such a study was possible thanks to our new agent-based model (Siudem et al. in Proc Natl Acad Sci 117:13896–13900, 2020, https://doi.org/10.1073/pnas.2001064117), which not only assumes that citations are distributed according to a mixture of the rich-get-richer rule and sheer chance, but also fits real bibliometric data quite well. We investigate which bibliometric indices have good discriminative power, which measures can be easily predicted as functions of other ones, and what implications to the research evaluation practice our findings have.

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