Bibliographic record
Abstract
Animal welfare has achieved significant global prominence for perhaps three reasons. First, several centuries of scientific research, especially in anatomy, evolutionary biology and animal behaviour, have led to a gradual narrowing of the gap that people perceive between humans and other species; this altered perception has prompted grass-roots attention to animals and their welfare, initially in Western countries but now more globally asthe influence of science has expanded. Second, scientific research on animal welfare has provided insights and methods for improving the handling, housing and management of animals; this 'animal welfare science' is increasingly seen as relevant to improving animal husbandry worldwide. Third, the development and use of explicit animal welfare standards has helped to integrate animal welfare as a component of national and international public policy, commerce and trade. To date, social debate about animal welfare has been dominated bythe industrialised nations. However, as the issue becomes increasingly global, it will be important for the non-industrialised countries to develop locally appropriate approaches to improving animal welfare, for example, by facilitating the provision of shelter, food, water and health care, and by improving basic handling, transportation and slaughter.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".