Relative Age Effects in Women's Rugby Union From Developmental Leagues to World Cup Tournaments
Bibliographic record
Abstract
UNLABELLED: Annual age cohort groupings promote relative age effects (RAEs), which often, inadvertently, create participation and attainment biases between relatively older and younger players within the same age cohort. In a globally evolving sport, women's rugby team selection practices may potentially bypass qualified players as a result of maturational differences. PURPOSE: Our study examined the prevalence of RAEs in women's rugby union. METHOD: Player data (age range = 4-21+ years) were gathered from the 2006 and 2010 Rugby World Cups (n = 498) and from Canadian (n = 1,497) and New Zealand (NZ; n = 13,899) developmental rugby leagues. RESULTS: Although no evidence of an RAE was found in the World Cup samples, chi-square analyses identified some typical and atypical patterns of RAEs at the developmental levels (w ≥ .3). Younger developmental groups displayed a typical RAE patterning with a greater representation of older players (Canadian 13-year-olds, w = .58; NZ 4-year-olds, w = .35), whereas older developmental groups displayed an atypical RAE patterning with a greater representation of younger players (Canadian 19-year-olds, w = .58; NZ 17-year-olds, w = .32). Further, a traditional RAE emerged in the Canadian 11- to 15-year-old age group, χ2(3) = 10.92, p < .05, w = .30. CONCLUSION: The lack of homogeneity of traditional RAEs across the sample questions the existence of a single, clear RAE in women's rugby. Some evidence of participation inequalities at the developmental levels suggests that further RAE research in more varied sociocultural contexts may be necessary.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".