Assessing animal welfare: different philosophies, different scientific approaches
Bibliographic record
Abstract
Attempts to improve animal welfare have commonly centered around three broad objectives: (1) to ensure good physical health and functioning of animals, (2) to minimize unpleasant "affective states" (pain, fear, etc.) and to allow animals normal pleasures, and (3) to allow animals to develop and live in ways that are natural for the species. Each of these objectives has given rise to scientific approaches for assessing animal welfare. An emphasis on health and functioning has led to assessment methods based on rates of disease, injury, mortality, and reproductive success. An emphasis on affective states has led to assessment methods based on indicators of pain, fear, distress, frustration and similar experiences. An emphasis on natural living has led to research on the natural behavior of animals and on the strength of animals' motivation to perform different elements of their behavior. All three approaches have yielded practical ways to improve animal welfare, and the three objectives are often correlated. However, under captive conditions, where the evolved adaptations of animals may not match the challenges of their current circumstances, the single-minded pursuit of any one criterion may lead to poor welfare as judged by the others. Furthermore, the three objectives arise from different philosophical views about what constitutes a good life-an area of disagreement that is deeply embedded in Western culture and that is not resolved by scientific research. If efforts to improve animal welfare are to achieve widespread acceptance, they need to strike a balance among the different animal welfare objectives.
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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 | 0.085 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.051 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".