Examining the relative age effect and influence of academic timing in Canadian interuniversity sport
Bibliographic record
Abstract
Relative age effects (RAE) are developmental advantages experienced by those born in the initial months after a predetermined cut-off date over their younger counterparts. When examining the RAE in an interuniversity setting, it is important to consider the academic timing of the student-athletes (Chittle et al., 2015; Dixon et al., 2013). Failing to consider this moderator can result in a skewed perception of the bias associated with relative age. Student-athletes are considered to be 'on-time' when their current year of athletic eligibility coincides with their expected year of athletic eligibility, based on their year of birth. Student-athletes are considered 'delayed' when their current athletic eligibility year corresponds with a younger cohort. This project examined the RAE and academic timing within nine of the 12 Canadian Interuniversity Sport (CIS) championship sports. A moderate RAE was seen among the entire sample of CIS student-athletes (males: X2 = 67.84, df = 3, p < 0.001, φ = 0.12; females: X2 = 40.87, df = 3, p < 0.001, φ = 0.10). Males are more likely to be delayed than females, and those student-athletes born in the later months of the year are more frequently delayed compared to their relatively older peers. Specifically, 73.33% of male CIS athletes are delayed with the most extreme examples seen in ice hockey (99.76%) and football (85.21%). Alternatively, only 39.50% of female CIS student-athletes are delayed. Based on these results, delaying one's athletic eligibly may be an effective method to reduce the disadvantages associated with being relatively younger.Acknowledgments: The authors would like to thank the Social Sciences and Humanities Research Council for funding this project.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".