University journals in the global academic publishing landscape: Mapping over 19,000 diverse titles
Bibliographic record
Abstract
Universities have been an instrumental part of the scholarly publishing landscape dating back several centuries, but comprehensive mapping of the presence of universities' involvement in publishing of journals is lacking. Using Ulrichsweb as the primary source and complementing it with data from Scopus, Web of Science, DOAJ and OpenAlex, we compiled a dataset of 19,414 active, peer-reviewed university journals from 148 countries using a multilingual identification method. The results reveal significant disparities in coverage: nearly three-quarters of the journals are indexed in OpenAlex, almost half in DOAJ, fewer than a quarter in Scopus, and fewer than a fifth on the Web of Science Core Collection. From a global perspective, university journals are heavily clustered to a few countries, notably the United States, Indonesia and Brazil. University journals are characterized by strong linguistic diversity, with more than a third publishing exclusively in non-English languages. The social sciences and humanities dominate the disciplinary profile. This study establishes a baseline for further research into bibliodiversity, equity and the role of universities in scholarly communication.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.036 | 0.077 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".