Gender, age, and sport differences in relative age effects among US Masters swimming and track and field athletes
Bibliographic record
Abstract
A relative age effect has been identified in Masters sports (Medic, Starkes, & Young, 2007 Medic, N., Starkes, J. L. and Young, B. W. 2007. Examining relative age effects on performance achievement and participation rates of Masters athletes. Journal of Sports Sciences, 25: 1377–1384. [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). Since gender, age, and type of sport have been found to influence the relative age effect in youth sports (Musch & Grondin, 2001 Musch, J. and Grondin, S. 2001. Unequal competition as an impediment to personal development: A review of the relative age effect in sport. Developmental Review, 21: 147–167. [Crossref], [Web of Science ®] , [Google Scholar]), we examined how these three variables influenced possible relative age effects among Masters swimmers and track and field athletes. Using archived data between 1996 and 2006, frequency of participation entries and record-setting performances at the US Masters championships were examined as a function of an individual's constituent year within any 5-year age category. Study 1 investigated the frequency of Master athletes who participated; Study 2 examined the frequency of performance records that were set across constituent years within an age category, while accounting for the distribution of participation frequencies. Results showed that a participation-related relative age effect in Masters sports is stronger for males, that it becomes progressively stronger with each successive decade of life, and that it does not differ across track and field and swimming. In addition, a performance-related relative age effect in Masters sport seems to be stronger for swimming than track and field, but it does not differ across gender and decades of life.
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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 | 0.006 |
| 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 | 0.000 | 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".