The Influence of Contextual Factors on the Relative Age Effect in Male International Rugby Union: The Impact of Sociocultural Influences and Playing Position
Bibliographic record
Abstract
The purpose of this study is not only to establish whether the relative age effect (RAE) exists in male international rugby union players, but also to investigate the impact of sociocultural influences (i.e., northern and southern hemispheres) and playing position (i.e., backs, forwards, and scrum-halves). The birth date and the playing position of 7144 senior male professional rugby players included in the rosters of the season 2020−2021 were collected from the top 10 nations of the World Rugby rankings (i.e., Argentina, Australia, England, France, Ireland, Japan, New Zealand, Scotland, South Africa, and Wales). Data were analyzed using a chi-square goodness-of-fit test to compare the observed and expected birth quarter (Q) distributions. Results showed that relatively older players were overrepresented in all the sample (p < 0.001; Q1 = 28.8% vs. Q4 = 20.3%). In players competing in both hemispheres, the RAE was weak despite a more pronounced RAE emerging for southern players. In addition, the RAE was present in backs and forwards, but inconsistent for scrum-halves. In general, the data suggest that relatively older players may be more likely to reach expertise at senior levels than their later-born peers, and that the effect was consistent in different sociocultural contexts as well as in backs and forwards.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".