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

Bibliographic Data

ID21441869
AuthorsNick Haupka (0009-0002-6478-6789, State and University Library, corresponding author)
Year2026
Volume131
Issue2
Pages925-941
Publication date2026-02-01
Open AccessYes
TypeARTICLE
VenueScientometrics (JOURNAL)
Journal identifiersISSN: 0138-9130 • E-ISSN: 1588-2861
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11192-025-05524-7
OpenAlexW7124559947
LanguageEN
References cited19

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

Tools

Open DOIOpen Access