Artículo

Hidden citations obscure true impact in science

Resumen

References, the mechanism scientists rely on to signal previous knowledge, lately have turned into widely used and misused measures of scientific impact. Yet, when a discovery becomes common knowledge, citations suffer from obliteration by incorporation. This leads to the concept of hidden citation, representing a clear textual credit to a discovery without a reference to the publication embodying it. Here, we rely on unsupervised interpretable machine learning applied to the full text of each paper to systematically identify hidden citations. We find that for influential discoveries hidden citations outnumber citation counts, emerging regardless of publishing venue and discipline. We show that the prevalence of hidden citations is not driven by citation counts, but rather by the degree of the discourse on the topic within the text of the manuscripts, indicating that the more discussed is a discovery, the less visible it is to standard bibliometric analysis. Hidden citations indicate that bibliometric measures offer a limited perspective on quantifying the true impact of a discovery, raising the need to extract knowledge from the full text of the scientific corpus.
Zeng, Lin (59304657200); Shi, Yihan (59304657100); Subatijang, Parhati (59350252900); Zhang, Lei (59350368800); Gao, Jian (59276502500); Sun, Rongxin (56574635100); Jiang, Kan (57370621600)
Global research trends and hotspots in rheumatoid arthritis joint replacement:Bibliometric analysis and visualization study
2025
10.1016/j.jor.2024.09.017
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205361667&doi=10.1016%2fj.jor.2024.09.017&partnerID=40&md5=4229a5e01e71046df9b23673cd8f5a62
Affiliated to the Sixth Affiliated Hospital of Xinjiang Medical University, China
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Scopus
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