Programas de financiamento ao atleta: uma perspectiva comparativa entre países
Bibliographic record
Abstract
From an evidence-based perspective, this study aimed to reflect on the scope of one of Brazil's policies to promote high-performance athletes, the Bolsa-Atleta Program. In order to investigate whether this program can be compared to the largest among its counterparts, exploratory research was conducted on data from the top 11 countries in terms of medal achievements at the 2016 Rio Olympic Games. These countries include the United States (121 medals), China (70 medals), Great Britain (67 medals), Russia (56 medals), Germany (42 medals), France (42 medals), Japan (41 medals), Australia (29 medals), Italy (28 medals), Canada (22 medals), South Korea (21 medals), and Brazil (19 medals). The quantity of medals, rather than the number of gold medals, was chosen as the classification considered by the Brazilian Olympic Committee (COB). Additionally, the methodology proposed by Scheerder, Claes & Willem (2017) was considered to establish comparisons between sports systems of different countries. As observed through the data analysis regarding international programs providing direct public funding to athletes, several similarities were found between these programs and the Brazilian Bolsa-Atleta. The conclusion reached is that it is possible to support such a statement, but it cannot be unequivocally claimed as "the biggest."
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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 | 0.025 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".