Bibliographic record
Abstract
Governance has become a hugely important issue within sport. Issues of corruption and ‘bad governance’ have become synonymous with some aspects of sport and closer scrutiny than ever before is being applied to ensure organisations are following international best practice in respect to how they are governed. As sport organisations are required to become more professional and to adopt a more transparent and accountable approach to their operations, it has become important for all students, researchers and professionals working in sport to understand what good governance is and how it should be achieved. This book is the first to examine sport governance around the world. It offers a series of in-depth case studies of governance policy and practice in 15 countries and regions, including the US, UK, China, Australia, Canada, South Africa, Latin America and the Middle East, as well as chapters covering governance by, and of, global sport organisations and international sport federations. With an introduction outlining the key contemporary themes in the study of sport governance, and a conclusion pointing at future directions for research and practice, this book is essential reading for any course on sport management, sport policy, sport development, sport administration or sport organisations, and for any manager or policy-maker working in sport and looking to improve their professional practice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.098 | 0.026 |
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".