Relative age effects in female sport contexts: A systematic review and meta-analysis of data (1984-2016)
Bibliographic record
Abstract
Subtle age differences can lead to immediate participation and long-term attainment influences in sport; commonly known as Relative Age Effects (RAEs; Barnsley et al., 1985). The prevalence of the RAE remains relatively undetermined for female sport contexts. Accordingly, the objective of this study was to examine female participation trends with respect to relative age in the published literature by evaluating the birth quartile distribution of athlete samples. Following PROSPERO (Reg. no. 42016053497) and PRISMA systematic search guidelines, 57 studies spanning 1984–2016 were identified and contained 308 independent samples across 25 sports. The overall prevalence and strength of the RAE across and within female sports contexts was determined, and moderator variables were assessed using odds ratio (OR; events vs. non-events) meta-analyses, applying an invariance random-effects model. The overall pooled estimate comparing the relatively oldest (Q1) v relatively youngest (Q4) suggested a small, but significant RAE (OR 1.25; 95% CI = 1.21-1.30; p = 0.01). Sub-group analyses revealed RAE magnitude was greater at pre-adolescent and adolescent age groups (
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.020 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".