Animal welfare requirements in publishing guidelines
Bibliographic record
Abstract
Descriptions of measures taken to optimize animal welfare are often absent from scientific reports of animal experiments. One reason may be that journal guidelines inadequately compel authors to provide such information. In this study, online English language versions of the 'Guidelines to authors' (GTAs) from 54 national biomedical journals were examined for neutral (unrelated to welfare) and non-neutral keywords referring to: animal welfare; the '3Rs'; the ARRIVE (2010) guidelines, and regulations pertaining to animal experimentation. Journals were selected from nine countries (UK, US, China, Canada, India, Brazil, Germany, Japan and Australia) and seven biomedical specialties (oncology, rheumatology, surgery, pharmacology, medicine, anaesthesia and veterinary medicine). Total GTA word counts varied from 1137 to 31,609. The keyword count identified per category were expressed per myriad (10,000) of total word count. One-way analyses of variance followed by post hoc Tukey pairwise comparisons revealed greater non-neutral per myriad word counts for (a) veterinary GTAs compared with medicine, oncology, rheumatology or surgery; (b) British, compared with Australian, Canadian, German and Japanese GTAs; and (c) no differences between non-neutral categories. The English language versions of GTAs of British and veterinary medical journals contain more words associated with animal welfare, the 3Rs and the ARRIVE guidelines than those from eight other countries and six other medical specialities. The exclusion of 'national' language versions from analysis precludes attempts to identify national differences in attitudes to laboratory animal welfare.
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 | 0.151 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.048 | 0.049 |
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".