Farm manager involvement in an equine on-farm welfare assessment: opportunities for education and improvement
Bibliographic record
Abstract
Abstract Previous work surveying equine professionals has suggested ignorance to be a primary cause of poor welfare within the industry, highlighting the importance of improving educational opportunities for industry stakeholders. This may be achieved through on-farm assessments designed to evaluate facilities and share resources with farm owners. While used extensively for evaluating production animal facilities, equine facilities are rarely formally assessed, making it important to determine how well those assessments would be received by equine owners and managers. As part of a larger project, an on-farm equine welfare assessment tool was pilot-tested on a sample of diverse horse farms (n = 26). Farm managers completed a self-assessment to determine their perception of their own farms with respect to animal welfare and then participated in the on-farm assessment process. Post-assessment interviews allowed participants to provide feedback regarding their experience. Farm managers most often underestimated the prevalence of structural issues in their facilities but were more discerning in management-related elements (eg stall cleanliness). Descriptive analysis indicated that farm managers felt that the on-farm assessment tool had the potential to be useful to newcomers to the industry and for a certification programme. Participants also highlighted areas that could make enforcing welfare standards an issue, such as horse and farm ownership. Understanding the perception of on-farm assessments is useful to gauge the potential success of animal care assessment programmes. If well-received, an industry-driven, on-farm welfare assessment has the potential to better educate horse farm managers and, by extension, improve the welfare of the animals under their care.
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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 | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".