Is OpenAlex suitable for research quality evaluation and which citation indicator is best?
比较OpenAlex与Scopus在引文分析中的表现，并测试原始计数、归一化引文分数等指标，发现OpenAlex在多数领域适用，且原始引文计数与质量判断的相关性不亚于归一化指标。
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) Normalized Citation Scores (NCS), and Normalized Log‐transformed Citation Scores (NLCS). Methods (1&2): The indicators calculated from 28.6 million articles were compared through 8704 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 normalization problems. The broadest OpenAlex classification scheme provides the best indicators. (2) Counterintuitively, raw citation counts are at least as good as nearly all field normalized 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 judgments. Field normalization seems ineffective because more cited fields tend to produce higher quality work, affecting interdisciplinary research or within‐field topic differences.