Examining Canadian Equine Industry Participants’ Perceptions of Horses and Their Welfare
Bibliographic record
Abstract
The diversity of the Canadian equine industry makes determining baseline attitudes and beliefs a challenge. Adult members of the Canadian equine industry (n = 901) participated in an online survey to report demographic information and views on the role of horses and their ability to experience affective states. Questions regarding the welfare state of all horses in the industry, potential ways to address welfare issues, and eight short scenarios were presented. Qualitative analysis, descriptive statistics, and a Chi-squared test for independence examined survey results and potential relationships. Participants strongly believed horses were capable of feeling positive and negative emotions, particularly pain and fear, but rarely were these beliefs reflected in their answers regarding aspects of equine welfare, which may be due to the large bias in these beliefs. Lack of knowledge and financial difficulties were noted as the biggest threats to equine welfare. Overall, there was widespread agreement regarding the presence of welfare issues within the equine industry, but opinions were more divided regarding how to best address them and which horses were most at risk. Understanding these perceptions may be useful to direct educational programs and industry-wide initiatives to address equine welfare through human behaviour change.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".