An examination of the relative age effect and academic timing in cis volleyball
Bibliographic record
Abstract
Sport organizations often use cut-off dates to equalize participation opportunities. In doing so, the relative age effect (RAE) becomes prevalent, as those born immediately after the cut-off date experience a developmental advantage over those born later in the year (Barnsley et al., 1985). Interuniversity sport occurs in an academic setting, where athletes can differ considerably in age; therefore, it is important to consider the academic timing (AT) of these student-athletes (Glamser & Marciani, 1992; Dixon et al., 2013). A student-athlete is considered 'on-time' if his or her birthdate and expected athletic eligibility status coincide, while a 'delayed' student-athlete will have an athletic eligibility correspond with a younger cohort. To date, few RAE investigations have examined volleyball, with none of these studies occurring in an interuniversity setting. Okazaki et al. (2011) explored the RAE among young female Brazilian volleyball players, and revealed a strong RAE, with more players born in the first quarter of 1991 and 1992 than any other quarter of the competition calendar. The purpose of the current study is to investigate the moderating effects of AT on the RAE among Canadian Interuniversity male (n = 1046) and female (n = 1374) volleyball players competing in the 2011-'12, 2012-'13, 2013-'14 seasons. A significant RAE was revealed across all three years for the overall male samples (p = 0.05), as well as for those considered 'on-time' (p = 0.05). Moreover, a significant RAE was noted for the overall (p = 0.018) and 'on-time' (p < 0.001) female samples in 2013-'14.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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".