Evaluating the linguistic coverage of <scp>OpenAlex</scp>: An assessment of metadata accuracy and completeness
Bibliographic record
Abstract
Abstract Clarivate's Web of Science (WoS) and Elsevier's Scopus have been for decades the main sources of bibliometric information. Although highly curated, these closed, proprietary databases are largely biased toward English‐language publications, underestimating the use of other languages in research dissemination. Launched in 2022, OpenAlex promised comprehensive, inclusive, and open‐source research information. While already in use by scholars and research institutions, the quality of its metadata is currently still being assessed. This paper contributes to this literature by assessing the completeness and accuracy of OpenAlex's metadata related to language, through a comparison with WoS, as well as an in‐depth manual validation of a sample of 6836 articles. Results show that OpenAlex exhibits a far more balanced linguistic coverage than WoS. However, language metadata are not always accurate, which leads OpenAlex to overestimate the place of English while underestimating that of other languages. If used critically, OpenAlex can provide comprehensive and representative analyses of languages used for scholarly publishing, but more work is needed at infrastructural level to ensure the quality of metadata on language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.083 | 0.311 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.034 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".