Relative age effects in fitness testing in a general school sample: how relative are they?
Bibliographic record
Abstract
When children or adolescents are grouped by age or year of birth, older individuals tend to outperform younger ones. These phenomena are known as relative age effects (RAEs). RAEs may result directly from differences in maturation, but may also be associated with psychological, pedagogic or other factors. In this article, we attempt to quantify RAEs in a simple fitness task and to identify the mechanisms operating. Data come from a 5-year study of 2278 individuals that included repeated administrations of the 20 m shuttle run. We use mixed-effect modelling to characterise change over time and then examine residuals from these models for evidence of an effect for age relative to peers or for season of birth. Age alone appears to account for RAEs in our sample, with no effects for age relative to peers or month of birth. Age grouping produces large disparities for girls under 12, moderate ones for boys of all ages and negligible ones for girls between 12 and 15. RAEs for this task and population appear to arise from simple age differences. Similar methods may be useful in determining whether other explanations of RAEs are necessary in other contexts. Evaluation processes that take age into account have the potential to mitigate RAEs in general settings.
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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 | 0.030 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".