Marketing passion: intrinsic motivation drives producers of standardbred race horses in a troubled industry
Bibliographic record
Abstract
The first to propose that marketers apply producers' intrinsic motivation (passion, love, joy) as a powerful marketing tool, this study recommends tackling the declining popularity of horseracing by translating to audiences the spiritual and emotional rewards experienced by breeders. This case study in Belleville, Canada assesses breeders' sources of de-motivation, range and relative contribution of extrinsic and intrinsic motivation, and planned persistence. Photographed farm tours, in-depth interviews and visual elicitation using breeders' photograph collections are triangulated by industry informant interviews, with ethnographic content analysis across methods and cases. Breeders provide unflinching insight into risks, costs and the state of the industry, yet demonstrate unequivocal persistence, confirming that intrinsic motivation dominates. Findings extend the definition of cultural industries to horseracing. Implications suggest marketers reconceptualise the product of horseracing, not simply as gambling, but as pageant and sporting event where competitors are bred with passion and appreciated for beauty, strength and speed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".