Mitigating Consequences of Prestige in Citations of Publications

TL;DR

This paper proposes a method to predict scientific paper citations using only observable pre-publication features, aiming to reduce bias caused by author and journal prestige in citation-based evaluations.

Contribution

It introduces a novel approach combining statistical models to accurately predict citations from pre-publication data, mitigating the Matthew effect in scientometric assessments.

Findings

High accuracy in citation prediction using only submission-stage features.

Effective reduction of prestige bias in citation-based evaluations.

Potential for more objective research funding decisions.

Abstract

For many public research organizations, funding creation of science and maximizing scientific output is of central interest. Typically, when evaluating scientific production for funding, citations are utilized as a proxy, although these are severely influenced by factors beyond scientific impact. This study aims to mitigate the consequences of the Matthew effect in citations, where prominent authors and prestigious journals receive more citations regardless of the scientific content of the publications. To this end, the study presents an approach to predicting citations of papers based solely on observable characteristics available at the submission stage of a double-blind peer-review process. Combining classical linear models, generalized linear models and utilizing large-scale data sets on biomedical papers based on the PubMed database, the results demonstrate that it is possible to make fairly accurate predictions of citations using only observable characteristics of papers excluding information on authors and journals, thereby mitigating the Matthew effect. Thus, the outcomes have important implications for the field of scientometrics, providing a more objective method for citation prediction by relying on pre-publication variables that are immune to manipulation by authors and journals, thereby enhancing the objectivity of the evaluation process. Our approach is thus important for government agencies responsible for funding the creation of high-quality scientific content rather than perpetuating prestige.