Understanding the multiple conceptions of animal welfare
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 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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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