Aiutare le riviste a migliorare la loro qualità editoriale: un'analisi dei dati sugli effetti dei nuovi criteri di DOAJ
Bibliographic record
Abstract
In 2013, Directory of Open Access Journals (DOAJ) expanded and updated its inclusion criteria and its journal evaluation process, ultimately removing a large number of journals that failed to submit an updated application. The present study examined the results of the new process and its capability to improve the quality of the directory and the reliability of the information contained in it. A dataset of 12.595 journals included in DOAJ, since its launch in 2003 until May 15th 2016, was examined and compared to other data. The number of journals deleted from DOAJ during this period is 3776; the majority of them (2851 journals) were excluded because publishers failed to complete the reapplication on time; 490 had ceased publication or were otherwise inactive; 375 were excluded for ethical issues; 53 because they were no longer open access or the content was embargoed, the final 7 were removed for other reasons. The top five countries in terms of the percentage of journals removed are: Japan (74% of journals removed); Pakistan (60%); Canada (51%); United States (50%); and Mexico (49%). Our study has shown that 158 of the removed journals are included in Beall’s lists; 1130 journals indexed in DOAJ are included in Scopus and/or JCR. Our analysis demonstrates that, thanks to the new acceptance criteria, to the improved screening process performed by national groups under the direction of the new management, there is a noticeable quality improvement of the journals indexed in DOAJ.
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 | 0.184 | 0.572 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.030 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".