Bibliographic record
Abstract
DOAJ is a unique search service for fully Open Access (OA) (no embargo) peer-reviewed scholarly journals, featuring options to include article or journal level metadata in other library search services. DOAJ’s over 9,000 journals represents about 27% of the world’s scholarly peer-reviewed journals, up from 10% in 2007, and the article-level search encompasses about 10% of global scholarly journal article production. All academic disciplines are represented; some (notably medicine), more so than others. With 128 countries and many languages represented, DOAJ is diverse and inclusive. DOAJ has a well-designed, clean, attractive, easy-to-use search interface. Suggestions for improvement include more reader-friendly organisation, and results-level metadata export for journals and articles. ADA compliance checking is currently in progress. DOAJ’s application form is long and complex and could benefit from streamlining. DOAJ is the premium venue for authors seeking quality Open Access journals to publish in. Its value for finding academic material is strong and growing. It is also a must-have for libraries. DOAJ membership for libraries, while optional, is of value to libraries for local OA promotion as well as promotion of the library through DOAJ’s popular website.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.473 | 0.422 |
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".