A reverse engineering approach to the suppression of citation biases reveals universal properties of citation distributions

TL;DR

This paper introduces a reverse engineering method to normalize citation counts across disciplines, revealing universal properties of citation distributions and enabling fairer scientific impact assessments.

Contribution

It develops a power-law based transformation to suppress disciplinary biases in citation counts, uncovering universal citation distribution properties.

Findings

Citation distributions follow a power-law transformation.

Transformation parameters are characteristic of each discipline.

Universal citation distribution properties are identified.

Abstract

The large amount of information contained in bibliographic databases has recently boosted the use of citations, and other indicators based on citation numbers, as tools for the quantitative assessment of scientific research. Citations counts are often interpreted as proxies for the scientific influence of papers, journals, scholars, and institutions. However, a rigorous and scientifically grounded methodology for a correct use of citation counts is still missing. In particular, cross-disciplinary comparisons in terms of raw citation counts systematically favors scientific disciplines with higher citation and publication rates. Here we perform an exhaustive study of the citation patterns of millions of papers, and derive a simple transformation of citation counts able to suppress the disproportionate citation counts among scientific domains. We find that the transformation is well described by a power-law function, and that the parameter values of the transformation are typical features of each scientific discipline. Universal properties of citation patterns descend therefore from the fact that citation distributions for papers in a specific field are all part of the same family of univariate distributions.