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
Standards and policies intended to safeguard nonhuman animal welfare, whether in zoos, farms, or laboratories, have tended to emphasize features of the physical environment. However, research has now made it clear that very different welfare outcomes are commonly seen in facilities using similar environments or conforming to the same animal welfare requirements. This wide variation is almost certainly due, at least in part, to the important effects of the actions of animal care staff on animal welfare. Drawing mostly on the farm animal literature, we propose that this "human dimension" of animal welfare involves seven components: (1) positive human-animal interaction, (2) consistency and familiarity of keepers, (3) treating animals as individuals and taking account of their personalities, (4) the attitudes and personalities of keepers, (5) the keepers' knowledge and experience, (6) the keepers' own well-being, and (7) the influence of facility design on how keepers and others interact with the animals. We suggest that attention to these human factors provides major scope for improving the welfare of animals in zoos.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it