Relative age effects and academic timing in Canadian interuniversity football
Bibliographic record
Abstract
Relative age effects (RAEs) explain the (dis)advantages individuals experience as a result of when they are born relative to a pre-determined cut-off date. Within an interuniversity setting, academic timing (AT) may moderate the RAE pattern due to some student-athletes having eligibility years that do not correspond with their birth years. The purpose of this study was to examine the influence of the RAE and AT on interuniversity football players. A series of chi-square goodness of fit tests (χ2) revealed no RAE when all student-athletes were analyzed together as well as among those who were delayed (i.e. eligibility years that correspond with a younger cohort), and a traditional RAE among those who were on-time (i.e. eligibility years that correspond with their year of birth). Student-athletes ranged between 1 and 12 years delayed, with the majority of these athletes being delayed by one (30.76%) or two years (30.97%). This study suggests that there may be advantages to student-athletes delaying their participation within football, as these delays may help mitigate the overall RAE by equalizing playing opportunities for relatively younger student-athletes. However, delaying eligibility raises concerns about equity, particularly for those progressing to interuniversity football directly out of high school who may have to compete for roster spots against student-athletes who may be up to 12 years delayed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".