Relative age effect in European female athletes in different sports; a systematic review
Bibliographic record
Abstract
The overall aim of the thesis was to determine the prevalence and magnitude of relative age effect (RAE) in European female sport via examination of published data spanning 2011-2021. The PRISMA systematic search guidelines were used to identify 14 studies out of 68 containing 53 independent samples across 9 sport types. Based on frequency, percentages were moderated by age group, competitive level, sport type, sport context, and study quality. Additionally, this study analysed whether RAE was present or not by comparing relatively older (born in the first quarter of the year; Q1) with younger (born in the last quarter of the year; Q4) girls and women across all European female sport contexts. The thesis shows that the overall estimate identified a small-moderate RAE among European female sports, within subgroup revealed RAE magnitude was higher in pre-adolescent (≤ 11 years) and adolescent (12-14 years) age groups and at higher competition levels. The results show that RAEs in females’ sports are common across the European contexts. A major factor influencing RAE magnitude are developmental levels, competition levels, and sport context demands. To prevent RAE-related participation and attainment inequalities, it is recommended that sport policy, organizational structure, and athlete development system structures be changed as well as practitioner intervention.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".