Cross-sectional age analyses of participatory trends in a Masters triathlon: Under-representation at relatively-older years in 5-year competitive categories
Bibliographic record
Abstract
The organization of adult sport in 5-year categories (35-39, 40-44, etc.) is intended to level the competitive field by controlling for age-related slowing, thereby motivating individuals to maintain participation. Despite this aim, international- and national-level Masters swimmers (Medic, Young, & Medic, 2010) and runners (Medic, Starkes, Weir, Young, & Grove, 2009) are more likely to be absent from competitive events in years 4 and 5 of an age-category, compared to years 1 and 2, when they are more likely to participate. This study investigated similar effects in a sample of diverse competitors (ranging from regional-level to elite) in a previously unexplored Masters sport (triathlon). Cross-sectional data (N = 12,283) for participants’ ages were compiled for seven consecutive years of the St. Anthony’s (Florida) Triathlon. Chi-square analyses were run separately by sex, for each 5-year age category from 35 until 59 yrs, to compare frequencies in each of the five constituent years in an age category. Results indicated a relative age effect for females at 45-49 (p < .001, ?=.21), 50-54 (p = .01, ?=.20), and 55-59 yrs (p < .01, ?=.29), and for males at 45-49 (p = .01, ?=.09), 50-54 (p < .01, ?=.14), and 55-59 yrs (p < .01, ?=.03). An inverse effect, showing under-representation in years 1 and 2, but overrepresentation in year 5 was seen for 30-34 yrs in males (p < .001, ?=.17) only. Discussion focuses on possible psychological explanations for such effects, gender roles and lifespan influences, and future methods to interpret such influences.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".