Presenting a classifier to detect research contributions in OpenAlex

TL;DR

This paper presents a high-accuracy classifier for distinguishing research articles from non-research content in OpenAlex, enhancing data quality for bibliometric analysis.

Contribution

It introduces a novel document type classifier that effectively identifies non-research content using open metadata, improving classification accuracy in bibliometric datasets.

Findings

F1-score of 0.95 for the classifier

Reclassified 10.75% of articles as non-research

Potential to improve data quality in OpenAlex

Abstract

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 detect non-research or editorial content within a set of classified articles and reviews (e.g. paratexts, abstracts, editorials, letters). The classifier 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%