How pervasive are relative age effects in secondary school education?
Bibliographic record
Abstract
Relative age effects (RAEs; R. H. Barnsley, A. H. Thompson, & P. E. Barnsley, 1985) convey school attainment (dis)advantages depending on whether one is relatively older or younger within annually age-grouped cohorts. In the present study, the authors examined the pervasiveness of RAEs by examining (a) attainment in 4 secondary school subjects, (b) attainment consistency across subjects, (c) pupils enrolled in gifted and talented programs, (d) pupils referred for learning support or identified as having special educational needs, and (e) whether RAEs were related to pupil attendance. For 2004-2005, attainment, program participation, and attendance data for 657 pupils (aged 11-14) at a secondary school in North England were analyzed. Relatively older pupils (i.e., September-November born) attained significantly higher in subjects (except for English), were more likely to attain consistently high scores across subject areas, and be enrolled in gifted and talented programs. In contrast, relatively younger pupils (i.e., January-August born) were overrepresented in learning support referrals and identified as having special educational needs, and were more likely to be among the lowest 20% of attainment and attendees, attending on average school 6 days less. RAEs are pervasive and systematic across the curriculum, implicating maturational and psychological mechanisms
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 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".