Bibliographic record
Abstract
Abstract Academics working on animal welfare typically consider the animal's affective state (eg the experience of pain), biological functioning (eg the presence of injuries), and sometimes naturalness (eg access to pasture), but it is unclear how these different factors are weighed in different cases. We argue that progress can be informed by systematically observing how ordinary people respond to scenarios designed to elicit varying, and potentially conflicting, types of concern. The evidence we review illustrates that people vary in how much weight they place on each of these three factors in their assessments of welfare in different cases; in some cases, concerns about the animal's affective state are predominant, and in other cases other concerns are more important. This evidence also suggests that people's assessments can also include factors (like the animal's relationship with its caregiver) that do not fit neatly within the dominant three-circles framework of affect, functioning and naturalness. We conclude that a more complete understanding of the multiple conceptions of animal welfare can be advanced by systematically exploring the views of non-specialists, including their responses to scenarios designed to elicit conflicting concerns.
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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 | 0.060 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| 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".