Bibliographic record
Abstract
Purpose: This study aimed to identify relative age effects of South Korea national male football teams that participated in 38 international competitions in age-specific categories from 2000 to 2018; U-16 (n=176), U-17 (n=82), U-19 (n=198), U-20 (n=147), and U-23 (n=166). Methods: Available information on birth-dates, heights, and body weights of South Korean elite male football players was collected from the official websites. Chi-square test was conducted and odds ratios were calculated with 95% confidence interval in order to examine differences of quarter distribution between expected and observed subgroups. Results: The birth distributions observed in each team were significantly different than those expected in general population of the same age (U-16: χ2=59.364, p＜0.05; U-17: χ2=36.829, p＜0.05; U-19: χ2=51.697, p＜0.05; U-20: χ2=39.531, p＜0.05) except U-23 (χ2=17.759, p=0.087). The magnitude of birth distribution was 3.2 times higher in the first quarter compared to that in the fourth quarter and was decreased in accordance with age. In accordance with age, the distribution of “competition age group” was significantly decreased in each team (U-16, 91%; U-17, 89%; U-19, 76%; U-20, 63%; U-23, 42%; p＜0.05) but that of “under-competition age group” was increased (U-16, 9%; U-17, 11%; U-19, 24%; U-20, 37%; U-23, 58%; p＜0.05). There is also significant difference in distribution between both “competition” and “under-competition age group” at the same tournament category (p＜0.05). Conclusion: Conclusively, these findings indicate that Korean players who are in the early stage of development have higher “relative age effects” than those in the late stage of development. This may implicate that it is necessary to develop strategies for relatively late-mature players who have potentials in terms of skills and intelligence of football.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".