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.

翻译：本文比较了(1)OpenAlex与Scopus的引文分析，测试其引文计数、文献类型/覆盖范围及学科分类；(2)三种基于引文的指标：原始计数、（学科与年份）标准化引文分数（NCS）及标准化对数转换引文分数（NLCS）。方法(1&2)：基于2,860万篇文献计算的指标，通过97,816篇英国研究卓越框架（REF）2021文献的两个黄金标准进行8,704次相关性比较。主要黄金标准为ChatGPT评分，次要标准为提交文献所属院系的REF2021专家评审平均分。结果：(1)OpenAlex提供比Scopus更优的引文计数，其包容性文献分类/范围未引起显著的学科归一化问题。最宽泛的OpenAlex分类方案能产生最佳指标。(2)反直觉的是，原始引文计数至少与几乎所有学科归一化指标相当，在单年度分析中表现更优，且NCS优于NLCS。(1&2)存在显著的学科差异。因此(1)OpenAlex适用于多数学科的引文分析，(2)主要引文指标与质量评估的关联呈现反直觉特征。学科归一化效果有限，因为高引文学科往往产出更高质量成果，这对跨学科研究或学科内主题差异产生影响。