A Reverse Engineering Approach to the Suppression of Citation Biases Reveals Universal Properties of Citation Distributions

Filippo Radicchi,Claudio Castellano,Bülent Yener

doi:10.1371/journal.pone.0033833

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.

Highlights

The use of bibliographic databases plays a practical, and crucial, role in modern science
Dividing raw citation counts by a scaling factor would correspond, in the logarithmic scale, to a horizontal rigid translation of the cumulative distribution
By looking at the figure, the cumulative distributions of the raw citation counts for papers published in journals within the subject-categories ‘‘Computer science, software engineering’’ and ‘‘Genetics & heredity’’ have a pretty similar shape, and the possibility to obtain a good collapse of the curves by rescaling citation counts seems reasonable

Summary

Introduction

The use of bibliographic databases plays a practical, and crucial, role in modern science. Citations between scientific publications are commonly used as quantitative indicators for the importance of scientific papers, as proxies for the influence of publications in the scientific community. General criticisms to the use of citation counts have been made [1–3], and the real meaning of a citation between papers can be very different and context dependent [4]. The more citations a paper has accumulated, the more influential the paper can be considered for its own scientific community of reference. The same unit of measure (i.e., a citation) is commonly used as the basis for the quantitative evaluation of individual scholars [5,6], journals [7], departments [8], universities and institutions [9], and even entire countries [10]. At the level of individual scientists, numerical indicators based on citation counts are evaluation tools of fundamental importance for decisions about hiring [11] and/or grant awards [12]

Methods

Results

Conclusion