Bibliographic record
Abstract
Horseracing, thoroughbred breeding and gambling on racing are global industries worth several hundred billion dollars. They are also industries facing serious challenges, from the rise of alternative forms of leisure gambling to concerns about the ethical treatment of animals in all equestrian sports. This book offers a broad-ranging examination of the contemporary horseracing industry, from geographical, economic, social, ethical and environmental perspectives. The book draws on in-depth, mixed-method research into the racing and breeding industries in the US, Australia, the UK, Canada and New Zealand, and includes comparative material on other key racing centres, such as Ireland, Singapore and Hong Kong. It explores the economic structure of the global racing business, including comparisons with other major international sport businesses and other equestrian sports. It examines the social and cultural roots of the sport through its association with, and impact on, rural places, communities and environments from Kentucky to Newmarket – highlighting racing’s particular blend of tradition and scientific and technological innovation. The book also explores the ethical issues at the heart of horseracing, from reproduction to the use of the whip, and the inescapable tension between the horse as an instrumentally valuable commodity and the horse as an intrinsically valuable animal with needs and interests. The Global Horseracing Industry concludes by considering alternative futures for this major international sports business. The book is illuminating reading for anybody with an interest in sport, business, cultural geography, animal studies, or environmental studies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.009 |
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".