Solving sport’s ‘relative age’ problem: a systematic review of proposed solutions
Bibliographic record
Abstract
Though the existence of Relative Age Effects (RAEs) has been documented through a multitude of studies spanning various sports and levels of play, application of solutions related to RAEs has been limited. In this review, the strengths and weaknesses of various proposed solutions to RAEs in youth sport are considered. Our objective was to identify, collate, and disseminate a comprehensive list of solutions related to the prevalence of RAEs in youth sport. English language, peer-reviewed articles were searched using the SPORTDiscuss database. Keywords ‘relative age’, ‘relative age effect*’, and sport* were used to locate research articles. The inclusion criteria were the following: (1) publication date between January 1980 and December 2018; (2) solutions were suggested related to RAEs. Sixty-three peer-reviewed publications contained proposed solutions to RAEs. Many solutions have been proposed to address RAEs in sport. Most are theoretical and there has been no attempt to implement them. Future research should test possible proposed solutions to RAEs in sport. However, implementing these solutions has the potential to both positively and negatively affect career and life outcomes for those athletes involved. Therefore, it is important to be cautious in how these possible solutions are tested.
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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 | 0.034 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".