Relative Age Effects in Women’s Ice Hockey: Contributions of Body Size and Maturity Status
Bibliographic record
Abstract
Research on relative age effects (RAEs) in women’s ice hockey is lacking data on participant characteristics, particularly body size and maturity status. The purposes of our study were to investigate RAEs in women’s ice hockey players from two countries, and to determine whether RAE patterns could be explained by chronological age, body size, and maturity status. Participants were 54 Swedish elite and 63 Canadian university players. Birthdates were coded by quartiles (Q1–Q4). Weight and height were obtained, and body mass index and chronological age were calculated for each player. Players recalled age at menarche, and maturity status was classified as early, average, or late relative to population-specific means. Chi-square (χ 2 ), odds ratios (OR), 95% confidence intervals (CI) and effect sizes (Cohen’s w ) were calculated using population data across quartiles and for pairwise comparisons between quartiles. Descriptive statistics and MANOVAs were run by quartile and by country. Significant RAEs were found for Canadian players across quartiles ( p < .05), along with a Q2 phenomenon (Q2: Q3, Q2: Q4, p < .05). Swedish players were overrepresented in Q3 (Q3: Q4, p < .05). Q4 was significantly underrepresented in both countries ( p < .05). The oldest, earliest maturing, and shortest players in both countries were clustered in Q2, whereas the next oldest and latest maturing Swedish players were found in Q3. Age, physical factors, and interactions may contribute to overrepresentations in Q2 and Q3. These findings do not suggest the same bias for greater relative age and maturity found in male ice hockey.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".