Is OpenAlex Suitable for Research Quality Evaluation and Which Citation Indicator is Best?

TL;DR

This study evaluates OpenAlex's suitability for research quality assessment by comparing citation indicators derived from OpenAlex and Scopus against expert and AI-based standards, revealing that raw citation counts are surprisingly effective.

Contribution

It provides a comprehensive comparison of citation indicators from OpenAlex and Scopus, demonstrating OpenAlex's effectiveness and challenging assumptions about the superiority of field-normalised metrics.

Findings

OpenAlex offers better citation counts than Scopus.

Raw citation counts perform as well or better than normalised indicators.

Field differences significantly impact citation-based quality assessments.

Abstract

This article compares (1) citation analysis with OpenAlex and Scopus, testing their citation counts, document type/coverage and subject classifications and (2) three citation-based indicators: raw counts, (field and year) Normalised Citation Scores (NCS) and Normalised Log-transformed Citation Scores (NLCS). Methods (1&2): The indicators calculated from 28.6 million articles were compared through 8,704 correlations on two gold standards for 97,816 UK Research Excellence Framework (REF) 2021 articles. The primary gold standard is ChatGPT scores, and the secondary is the average REF2021 expert review score for the department submitting the article. Results: (1) OpenAlex provides better citation counts than Scopus and its inclusive document classification/scope does not seem to cause substantial field normalisation problems. The broadest OpenAlex classification scheme provides the best indicators. (2) Counterintuitively, raw citation counts are at least as good as nearly all field normalised indicators, and better for single years, and NCS is better than NLCS. (1&2) There are substantial field differences. Thus, (1) OpenAlex is suitable for citation analysis in most fields and (2) the major citation-based indicators seem to work counterintuitively compared to quality judgements. Field normalisation seems ineffective because more cited fields tend to produce higher quality work, affecting interdisciplinary research or within-field topic differences.