An exploration of industry expert perception of Canadian equine welfare using a modified Delphi technique
Bibliographic record
Abstract
The diversity of sectors that comprise the equine industry makes reaching a consensus regarding welfare issues a challenge. To allow for productive discussion, equine professionals (n = 34) chosen to represent the diverse specializations from across Canada were surveyed using the Delphi technique-a survey technique employing multiple, iterative "rounds" to consolidate viewpoints-to gather and consolidate information regarding areas of welfare concern in the Canadian equine industry. Only participants who completed the prior round could participate in subsequent rounds. In the first round, respondents were asked to identify examples of welfare issues. Qualitative analysis was used to sort and group answers based on their similarities. Participants identified 12 welfare issues best addressed at the individual horse level, and an additional 12 welfare issues best addressed at the industry level. In the second (n = 24) and third (n = 14) rounds, welfare issues, solutions, and potential motives were consolidated based on order ranking. Themes of "ignorance" and "lack of knowledge" identified throughout all three rounds were cited as both potential risks to welfare as well as motives leading to poor welfare situations. Responses in this study suggest that in order to improve the welfare of equids in the Canadian industry, equine professionals propose that a greater effort is required to help educate industry members and stakeholders such that, through daily routine care and management, higher standards of welfare can be attained.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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 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".