Talent Selection and Management in View of Relative Age: the Case of Swimming
Bibliographic record
Abstract
Abstract Based on our empirical research, through the analysis of the birthdates of young competitive swimmers, the present paper aims to examine the system of talent selection and management in Hungarian competitive swimming complemented with a new element. The research population consisted of the registered junior competitive swimmers participating in the new talent management program of the Hungarian Swimming Association (N=235; average age: 11.44) due to the decision of the Coaches’ Committee. Our research was based on the analysis of documents and databases. Besides the descriptive statistics, Chi-square tests and the Kruskal-Wallis test were applied. The results show that swimmers born in the first three months of the year are still more likely to be recruited in the program than their relatively younger counterparts. Furthermore, as a potential effect of the new program, the dominance of the first quarter of the year is also characteristic among those eligible for the next level of talent management. The new selection system of Hungarian swimmers is still highly sensitive to the relative age. Thus, it is recommended to further investigate the functioning of the new talent management program in terms of selection and success.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".