The Long-Term Impact of the Youth Olympic Games Culture and Education Program on Turkish Athletes
Bibliographic record
Abstract
Since 2010, the International Olympic Committee (IOC) has been organizing the Youth Olympic Games (YOG), a recent addition to the Olympic family, designed for talented athletes aged 15 to 18. These games not only encompass competitive events but also include cultural and educational Programmes. The primary objective of this study is to explore the long-term educational, social, and cultural impacts of the YOG on Turkish athletes. A total of 11 Turkish athletes (comprising 4 Olympic athletes, 4 elite level athletes, and 3 retired athletes) were selected through purposive and snowball sampling from a pool of 184 Turkish athletes who had participated in various YOG events. Data collection involved semi-structured interviews and retrospective methods, while thematic analysis was utilized for data analysis. Memory checking, critical friends, and prolonged engagement were employed to ensure trustworthiness. The study's findings indicate that the YOG had a lasting influence on Turkish athletes, inspiring many to become both Olympians and ambassadors for the Olympic Values. The formation of friendships and meaningful social and cultural interactions during the YOG served as motivation to pursue excellence in their athletic careers. Notably, even retired athletes continued to engage in activities to promote the Olympic Values and sports after their YOG experience.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".