WikiEdge:ArXiv速遞/2025-02-25
摘要
- 原文標題：Is OpenAlex Suitable for Research Quality Evaluation and Which Citation Indicator is Best?
- 中文標題：OpenAlex 是否適合研究質量評估？哪種引用指標最佳？
- 發布日期：2025-02-25 18:21:30+00:00
- 作者：Mike Thelwall, Xiaorui Jiang
- 分類：cs.DL
- 原文連結：http://arxiv.org/abs/2502.18427v1
原文摘要：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. 中文摘要：本文比較了（1）使用OpenAlex和Scopus進行引文分析，測試它們的引用計數、文檔類型/覆蓋範圍和學科分類，以及（2）三種基於引文的指標：原始計數、（領域和年份）標準化引文得分（NCS）和標準化對數轉換引文得分（NLCS）。方法（1&2）：通過對28.6百萬篇文章的指標計算，並在兩個黃金標準上進行了8,704次相關性比較，這些標準涉及97,816篇英國研究卓越框架（REF）2021的文章。主要黃金標準是ChatGPT評分，次要標準是提交文章的部門的平均REF2021專家評審得分。結果：（1）OpenAlex提供的引用計數優於Scopus，其包容性文檔分類/範圍似乎不會導致顯著的領域標準化問題。最廣泛的OpenAlex分類方案提供了最佳指標。（2）與直覺相反，原始引用計數至少與幾乎所有領域標準化指標一樣好，並且在單一年份中表現更好，而NCS優於NLCS。（1&2）存在顯著的領域差異。因此，（1）OpenAlex適用於大多數領域的引文分析，（2）主要的基於引文的指標似乎與質量判斷相反。領域標準化似乎無效，因為引用較多的領域往往產生更高質量的工作，影響跨學科研究或領域內主題差異。