Relative age effects and elite Canadian women's ice hockey
Bibliographic record
Abstract
The relative age effect (RAE) suggests that athletes born earlier in a sport's selection year are advantaged in terms of selection and playing opportunities. While prevalent in men's sports, little work has been directed at examining the RAE in women's sports. The studies to date present an equivocal pattern of findings across a variety of women's sports and competition levels. The purpose of the present study was to examine the prevalence of RAEs in elite Canadian women's ice hockey. Relative age and player position information on 660 female hockey players, across two levels of playing competition, were gathered from the Hockey Canada website. The chi-square analyses revealed no differences in the distribution of relatively older and younger players across the levels of competition, and overall there was a higher proportion of players born in Q2 (32.88%) than in Q4 (16.82%). Within the context of player position, both forwards and defense followed this same distribution. While different than the patterning of RAEs seen in men's hockey, the overall trend is similar in that opportunities for females to participate at an elite level are concentrated among relatively older players. The mechanism underlying this distribution is not related to maturational differences in height and weight. We anticipate that the increasing growth and popularity of women's ice hockey will result in the RAE becoming even more pronounced at all levels of participation.Acknowledgments: Hockey Canada
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".