Assessing Animal Welfare at the Farm and Group Level: The Interplay of Science and Values
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it