ArticlePloS one2012
A reverse engineering approach to the suppression of citation biases reveals universal properties of citation distributions.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends and biases in the social cost of carbon.Annals of the New York Academy of Sciences · 2025Pooled it
- A network-based normalized impact measure reveals successful periods of scientific discovery across discipline.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- On entropy research analysis: cross-disciplinary knowledge transfer.Scientometrics · 2018Article
- Article
- On a heuristic point of view concerning the citation distribution: introducing the Wakeby distribution.SpringerPlus · 2015Article
- The Matthew effect in empirical data.Journal of the Royal Society, Interface · 2014Review
- Article
- A proposal for a novel impact factor as an alternative to the JCR impact factor.Scientific reports · 2013Article
- The measurement of the effect on citation inequality of differences in citation practices across scientific fields.PloS one · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.