The Constituent Year Effect: Relative Age Disparities in Australian Masters Track and Field Athletic Participation
Bibliographic record
Abstract
The constituent year effect, a source of relative age disparities, in masters sport has been demonstrated mainly amongst North American samples. Thus, the purpose of this study was to examine whether a participation-related constituent year effect exists among athletes (n = 6492) competing in Australian Masters Athletics competitions between 2000 and 2014. The results indicated that a participation-related constituent year effect was observed as the likelihood of participating was significantly higher for masters athletes in their first and second constituent year of any five-year age category (p < 0.0001) and was lower when they were in the fourth or fifth constituent year. The results also indicated this effect is influenced by gender and age. Specifically, the effect was significant for both male (p < 0.0001) and female (p < 0.001) masters athletes; as well during the third, sixth, seventh, and eighth + decades of life (all ps < 0.001). These data demonstrate that despite masters sport being an avenue for promotion of participation and overall health, there is potential for improving how competitive organizational strategies are implemented given the recurring intermittent patterns of participation associated with five-year age brackets which are likely to compromise benefits.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".