Participation-related relative age effects in Masters swimming: A 6-year retrospective longitudinal analysis
Bibliographic record
Abstract
Medic and colleagues (Medic, Starkes, & Young, 2007) found that relatively younger cohorts of Masters athletes had higher participation rates and achieved higher performances compared with a relatively older cohort. Considering that Medic and colleagues' (2007) study was cross-sectional in nature, the purpose of this investigation was to employ a retrospective longitudinal study design to examine the participation rates of Masters swimmers as a function of an individual's constituent year within any 5-year age category over a period of 6 years. Using archived data from the 2003 to 2009 US Masters Short Course National Championships, swimmers' attendance was followed for a period of six consecutive years. Results indicated that a participation-related relative age effect was observed among swimmers who, over a period of 6 years, competed in either at least one championship (N = 2596; Cochran's Q₄ = 64.16, r(s) = -0.92, both P < 0.0001) or at least three championships (N = 441; Cochran's Q₄ = 47.51, r(s) = -0.91, both P < 0.0001). Overall, effect size analyses indicated that the odds of a Masters swimmer participating in the championship during the first constituent year of any 5-year age category was more than two times greater than the odds of that athlete participating during the fifth constituent year.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".