Relative age effects on speed trials in Brazilian athletics
Bibliographic record
Abstract
BACKGROUND: Relative age effect (RAE) is a concept related to the possible advantage that older athletes would have over younger ones within the same category. Although many studies have approached this subject in individual sports, there are few clippings by events within the sport. More detailed analyses are necessary for a better understanding of how RAE behaves in sports, especially in athletics, the subject of this study. The objective of this study was to analyze the RAE on speed in track and field events as a whole, separating the flat races from the hurdles races. METHODS: The Brazilian Ranking of Brazilian Athletics Confederation was used for data analysis, and the sample was composed of the 50 best-placed marks in the ranking of speed events in athletics in the categories Under(U)-16 and U-18 (female and male). Statistical analysis was calculated by chi-square, and the effect size was checked by Cramer's V. Likelihood-ratio test (L-Ratio) assessed the probability of the RAE occurring in the total sample and by age groups. RESULTS: In the total sample the results pointed to the emergence of RAE in males in both categories (U-16: p < 0.001; V: 0.13; L-Ratio: 3.64, U-18: p < 0.001; V: 0.13; L-Ratio: 3.80), whereas in females no such effect was found in any category (U-16: p = 0.6; V: 0.09; L-Ratio: 0.09, U-18: p = 0.6; V: 0.07; L-Ratio: 0.12). When the results were separated by type of event, there was only a RAE in the shallow event in the U-18 female category (p = 0.3; V: 0.11; L-Ratio: 8.72). CONCLUSION: The results allow us to conclude that there is a RAE in the speed trials of Brazilian athletics in the U16 and U18 categories for men, while this effect appears only in the shallow trials of the U18 category for women, indicating that the RAE has incidence when there is more participation and competition in the sport.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".