A RESEARCH ON THE ROLE OF RELATIVE AGE EFFECTIVENESS IN SPORTS TERMINATION
Bibliographic record
Abstract
Development in children; may vary according to the growth stages. Therefore, the age factor is one of the most controversial topics in child training. Children belonging to the same age group develop at different speeds and at different times according to each other. Because of the rapid development of childhood during puberty, even during the birth of the age of the developmental superiority can be seen even at birth. This suggests that children may fall into disadvantages within the same age group, according to the birth month. It is believed that children are affected by this disadvantage as well as external factors in sport finishing. The Naili Moran Age Groups Races organized by the Turkish Athletics Federation (TAF) to strengthen infrastructure work include sportspeople aged 12-15 years. The study was based on the participation of these age groups for the 2016 and 2017 seasons, and the data was downloaded from the TAF website. In the study, the achievement levels of 328 athletes (n = 172) and male (n = 156) were examined. In this study, four quarters of a year were separated and success and failure rates of the athletes were determined according to the quarters. For statistical analysis, in the SPSS 22.0 program, independent samples t test, one way ANOVA and LSD correction tests were used. When the months of birth of the male and female athletes were examined, it was seen that the births were higher in the first quarter of the year for each age group. It was observed that the athletes born in the first quarter were more successful than the athletes born in the other quarters. It was determined that the athletes who continued the sports in the following year (2017) were successful and the athletes who had dropped in the following year had a failed year in the 2016 season. It is thought that the bad grades obtained in the termination of the sport are influenced and the success status is due to the Relative Age Effect. Article visualizations:
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".