Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it