An Exploration of Industry Expert Perception of Equine Welfare Using Vignettes
Bibliographic record
Abstract
= 14) were presented with twelve short scenarios in which a horse's welfare could be compromised. They were asked to rank each scenario (with 0 indicating no welfare concerns and 5 indicating a situation where immediate intervention was necessary), provide justification for their ranking, and give examples of what might have been the motivation behind the scenario. The wide range within vignette scores demonstrated the diversity of opinion even among a relatively small group of equine professionals. Qualitative analysis of responses to vignettes suggested that respondents typically ranked situations higher if they had a longer duration and the potential for greater or longer-lasting consequences (e.g., serious injury). Respondents were also the most sensitive to situations in which the horse's physical well-being (e.g., painful experience) was, or could be, compromised. Financial reasons, ignorance, and human convenience were also areas discussed as potential motivators by survey respondents. Overall, responses from the vignettes allowed for a picture of welfare perception based on personal values.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".