A mixed-methods analysis of similarities and differences in animal shelter staff, dog behavior professionals, and the public in determining kenneled dog welfare
Bibliographic record
Abstract
The ability of animal shelter employees to identify poor welfare states in kenneled dogs is crucial for the mitigation of suffering. Animal shelter employees (n = 28), animal behavior professional (n = 49), and the general public (n = 41) watched 10 videos of kenneled dogs then rated the welfare of the dogs, stated the rationale for their score, indicated how they would improve the welfare, and rated the feasibility of improvements. Professionals gave slightly lower (poorer) welfare scores compared to the public (z = -1.998, p = 0.046). Shelter employees (z = -5.976, p < 0.001) and professionals (z = 9.047, p < 0.001) used body language and behavior to explain their welfare scores more than the public. All three populations mentioned the addition of enrichment to improve the welfare, however, shelter employees (z = -5.748, p < 0.001) and professionals (z = 6.046, p < 0.001) mentioned it significantly more. There were no significant differences in the perceived feasibility of changes. Future research should explore possible reasons for the lack of welfare improvements within animal shelters.
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.
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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.009 |
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".