Medal Shares in Winter Olympic Games by Sport: Socioeconomic Analysis After Vancouver 2010
Why this work is in the frame
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Bibliographic record
Abstract
Objectives The quantitative analysis of the medal shares (MSs) in recent Winter Olympic history by sport and country will identify the relevant socioeconomic factors that are likely to derive policy issues. Methods Econometric modeling and concentration indices are used to predict and assess MSs by sport and country. The development of a mathematical routine to convert MSs to counts and the proposal of a modified Herfindahl index has added robustness to the analysis. Results The deviations of the predictions for Vancouver (2010) 〈 http://www.vancouver2010.com/ 〉 from the observed values pinpoint significant factor differences among the sports. Medal concentrations are different by sport in the number of countries and their names. Conclusions Tradition and geography are primary factors affecting the medal‐winning process. Concerning feasible policies for newcomers, nationalization of athletes, development of talent identification systems, and fostering the participation in sports without a “monopolistic” winner could become short ‐ and long‐run successful paths.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it