Presenting a classifier to improve the identification of research journal publications in OpenAlex

Dados Bibliográficos

ID21441869
AutoresNick Haupka (0009-0002-6478-6789, State and University Library, autor correspondente)
Ano2026
Volume131
Fascículo2
Páginas925-941
Data de publicação2026-02-01
Open AccessSim
TipoARTICLE
PeriódicoScientometrics (JOURNAL)
Identificadores do periódicoISSN: 0138-9130 • E-ISSN: 1588-2861
EditoraSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11192-025-05524-7
OpenAlexW7124559947
IdiomaEN
Referências citadas19

This paper introduces a document type classifier with the purpose to optimise the distinction between research and non-research journal publications in OpenAlex. Based on open metadata, the classifier can identify non-research or editorial content within a set of classified articles and reviews (e.g. paratext , abstracts , editorials , letters ), which for example is relevant for bibliometric studies, university rankings and academic procedures. In this respect, OpenAlex shows issues in classifying journal research contributions, tending to overestimate them compared to other databases, due to the promotion of non-research items to research items. The classifier presented in this study achieves an F1-score of 0.95, indicating a potential improvement in the data quality of bibliometric research in OpenAlex when applying the classifier on real data. In total, 4,589,967 out of 42,701,863 articles and reviews could be reclassified as non-research contributions by the classifier, representing a share of 10.75

Bibliometrics · Citation · Classifier (UML) · Open research · Scientometrics · Academic Publishing and Open Access · scientometrics and bibliometrics research · Text and Document Classification Technologies

Ferramentas

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