Examining the geographic and linguistic coverage of gold and diamond open access journals in OpenAlex, Scopus, and Web of Science
Bibliographic record
Abstract
Abstract Diamond open access (OA) journals are a publishing model that is free for both authors and readers, but their lack of indexing in major bibliographic databases compared to gold journals presents challenges in assessing their uptake. Furthermore, the characteristics of diamond journals, such as language and country of publication, have often been used to support the argument that they are more diverse and serve national research communities. However, there is a notable lack of empirical evidence regarding their geographical and linguistic characteristics. Using the Directory of Open Access Journals (DOAJ) as a benchmark, we investigate OA journals through their coverage in OpenAlex, Web of Science (WoS) and Scopus by field, country, language. The results show the lower coverage of diamond journals in WoS and Scopus and confirms the national scope of diamond journals compared to gold. The share of English-only journals is considerably higher among gold journals in every database. High-income countries have the highest share of authorship in every domain and type of journal, except for diamond journals in the social sciences and humanities. Understanding the current landscape of diamond OA indexing can aid the scholarly communications community and decision-makers in advancing policy and practices toward more inclusive OA models.
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 | 0.007 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".