A review of proposed solutions to relative age effects in sport: Preliminary results
Bibliographic record
Abstract
Relative age effects (RAEs) generally describe the over-representation of relatively older athletes in competitive youth sport, and later in elite adult sport. These effects have been described as a bias or error in talent identification and development practice. The purpose of this study was to provide the first systematic review of proposed solutions to RAEs in sport. First a PRISMA systematic review was conducted to compile a collection of English language peer reviewed journal articles on RAEs in sport using Web of Science and SPORTDiscus (as well as gray searching). Search terms included relative age, relative age effects, and sport. Once compiled and reviewed against the inclusion and exclusion criteria 149 articles (original research and reviews) were retained for inspection. Retained articles were then each searched using a quasi-PRISMA process, utilizing search terms that emphasize preventing RAEs (i.e., solutions, fix, strategy, eliminate, prevent, bias, selection) and an inclusion criteria (solutions had to be related to proposals for youth sport). Using this process, several proposed solutions were compiled. These proposed solutions ranged from non-technical pedagogical and coach-education initiatives to numerous technical solutions (e.g. different ways of rotating cut-off dates and cohorts, and age-standardized performance weighting). Each proposed solution is discussed with respect to strengths, limitations and feasibility, as well as its integration with theoretical model of RAEs in sport proposed by Wattie et al. (2015).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.112 | 0.305 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".