Open Access of Publications by Veterinary Faculty in the United States and Canada
Bibliographic record
Abstract
The free availability of full-text veterinary publications in MEDLINE-indexed journals by US and Canadian veterinary faculty from 2006-7 was determined. Additionally, publishing databases were searched to obtain general statistics on veterinary publishing. A survey of institutional initiatives to promote open-access journals and institutional repositories was also performed. Veterinary faculty published a total of 4,872 articles indexed by MEDLINE in 679 different journals. Of these articles, 1,334 (27%) were available as free full text and were published in 245 different journals. Although 51 veterinary-specific journals offering immediate and free full-text access were identified, few articles in this study appeared in these titles. Rather, most free scholarly articles by veterinary faculty appeared in journals with an embargo period. Academic veterinary institutions may want to recommend acceptance of alternate forms of information dissemination (such as open-access journals and journals published only digitally) to encourage greater global dissemination of their research findings. The promotion and use of digital institutional repositories is also an area for future investment and warrants additional research.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.045 | 0.095 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".