Article's Scientific Prestige: measuring the impact of individual articles in the Web of Science

TL;DR

This paper introduces the Article's Scientific Prestige (ASP) metric, based on eigenvector centrality, to evaluate individual article impact across disciplines, offering a more consistent alternative to citation counts.

Contribution

The paper proposes the ASP metric for assessing scientific impact, which considers both direct and indirect citations, improving cross-disciplinary evaluation over traditional citation counts.

Findings

ASP and #Cit are often misaligned, especially for less cited articles.

ASP provides more persuasive rankings than #Cit for less cited articles.

Journal grade does not accurately reflect individual article impact.

Abstract

We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and 1.45 billion citations on 254 subjects from 1981 to 2020. We proposed the Article's Scientific Prestige (ASP) metric and compared this metric to number of citations (#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to #Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and #Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than #Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference.