Successful Elite Sport Policies
Bibliographic record
Abstract
How can nations improve their chances of winning medals in international sport? This book deals with the strategic policy planning process that underpins the development of successful national elite sport development systems. Drawing on various international competitiveness studies, it examines how nations develop and implement policies that are based on the critical success factors that may lead to competitive advantage in world sport. An international group of researchers joined forces to develop theories, methods and a model on the Sports Policy factors Leading to International Sporting Success (SPLISS). The book presents the results of the large-scale international SPLISS-project. In this project the research team identified, compared and contrasted elite sport policies and strategies in place for the Olympic Games and other events in 15 distinct nations. With input from 58 researchers and 33 policy makers worldwide and the views of over 3,000 elite athletes, 1,300 high performance coaches and 240 performance directors, this work is the largest benchmarking study of national elite sport policies ever conducted. The nations taking part in SPLISS are: • Americas: Brazil and Canada • Asia: Japan and South Korea • Europe: Belgium (Flanders & Wallonia), Denmark, Estonia, Finland, France, the Netherlands, Northern Ireland, Portugal, Spain, Switzerland • Oceania: Australia
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".