Assessing Animal Welfare at the Farm and Group Level: The Interplay of Science and Values
Bibliographic record
Abstract
Abstract In the social debate about animal welfare we can identify three different views about how animals should be raised and how their welfare should be judged: (1) the view that animals should be raised under conditions that promote good biological functioning in the sense of health, growth and reproduction, (2) the view that animals should be raised in ways that minimise suffering and promote contentment, and (3) the view that animals should be allowed to lead relatively natural lives. When attempting to assess animal welfare, different scientists select different criteria, reflecting one or more of these value-dependent views. Even when ostensibly covering all three views, scientists may differ in what they treat as inherently important versus only instrumentally important, and their selection of variables may be further influenced by a desire to use measures that are scientifically respected and can be scored objectively. Value assumptions may also enter animal welfare assessment at the farm and group level (1) when empirical data provide insufficient guidance on important issues, (2) when we need to weigh conflicting interests of different animals, and (3) when we need to weigh conflicting evidence from different variables. Although value assumptions cannot be eliminated from animal welfare assessment, they can be made more explicit as the first step in creating animal welfare assessment tools. Different value assumptions could lead to different welfare assessment tools, each claiming validity within a given set of assumptions.
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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 | 0.049 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".