The Global Sporting Arms Race: an International Comparative Study on Sports Policy Factors Leading to International Sporting Success
Bibliographic record
Abstract
Over the last few decades the power struggle between nations to win medals in major international competitions has intensified. This has led to national sports organisations and governments throughout the\nworld spending increasing sums of money on elite sport. Several nations have indeed shown that accelerated funding in elite sport can lead to an increase of medals won at the Olympics. Nevertheless, in spite of increasing competition and the homogenisation of elite sports systems, the optimum strategy for delivering international success is still unclear. There is no model for comparing, and increasing, the efficiency and effectiveness of elite sport investments and management systems. This makes it difficult for sports managers and policy makers to prioritise and to make the right choices in elite sports policy. This book presents an international comparison of elite sport policies in six nations (Belgium, Canada, Italy, the Netherlands, Norway and United Kingdom). Over 1,400 athletes, coaches and performance directors in these nations have provided information on the climate to perform at the highest level of elite sport in their country. Over a hundred criteria are evaluated and compared using a scoring system in nine sport policy areas. This book is aimed at sports professionals, academics and politicians seeking a better understanding of the\nfactors that lead to international sporting success and seeking insights in future sport policy developments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".