A wicked problem: Systemic issues surrounding Canadian equestrian dressage and dressage horse welfare
Bibliographic record
Abstract
Competitive dressage's social licence to operate is in jeopardy due to ethical concerns surrounding the use of horses for dressage. There is limited research that contributes to our understanding of Canadian equestrian perspectives on the use of horses in dressage. The objectives of this study were to: (1) explore the cultural context of the Canadian dressage industry, including how horse well-being is integrated within the culture; and (2) investigate coaches' and riders' perceptions and experiences with the use of horses for dressage. An ethnographic case study approach was employed, where MR spent 2-6 weeks with each of the four participating Equestrian Canada Certified dressage coaches and their riders (at least four riders per coach for a total of 19 riders). Data collection included direct observation, recording field notes and conducting at least one in-depth interview with each coach and rider. Interviews and field notes were analysed using reflexive thematic analysis leading to the development of three themes: (1) the systems that participants operate within; (2) how these systems foster a culture of contradiction in the industry; and (3) the 'equestrian dilemma' highlighting how participants navigate their love for horses with their horses' well-being amid the sport's demands. The three themes portray that the issues faced by the dressage industry may be rooted in systemic problems and could be described as a 'wicked problem'. These results aim to inform future research initiatives that promote a holistic understanding of the challenges faced by the dressage industry and promote systems thinking solutions.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.031 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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 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".