Universality of Performance Indicators based on Citation and Reference Counts

TL;DR

This paper demonstrates the universality of certain bibliometric indicators, including a new index combining citation and reference counts, across different datasets, disciplines, and publication times, facilitating cross-disciplinary comparisons.

Contribution

It introduces a new bibliometric index considering citations and references, and shows its universal behavior across diverse datasets and disciplines.

Findings

The new index exhibits universal distribution patterns.

Distribution fits a lognormal with variance ~1.3.

Averages of log indices enable cross-disciplinary comparisons.

Abstract

We find evidence for the universality of two relative bibliometric indicators of the quality of individual scientific publications taken from different data sets. One of these is a new index that considers both citation and reference counts. We demonstrate this universality for relatively well cited publications from a single institute, grouped by year of publication and by faculty or by department. We show similar behaviour in publications submitted to the arXiv e-print archive, grouped by year of submission and by sub-archive. We also find that for reasonably well cited papers this distribution is well fitted by a lognormal with a variance of around 1.3 which is consistent with the results of Radicchi, Fortunato, and Castellano (2008). Our work demonstrates that comparisons can be made between publications from different disciplines and publication dates, regardless of their citation count and without expensive access to the whole world-wide citation graph. Further, it shows that averages of the logarithm of such relative bibliometric indices deal with the issue of long tails and avoid the need for statistics based on lengthy ranking procedures.