The influence of birth date and city of development on youth sport participation
Bibliographic record
Abstract
A growing body of literature highlights the critical role that an individual's early sport environment plays in facilitating athletic development. Indeed, evidence exists to suggest that contextual factors relating to young athletes' initial exposure to sport may have an important impact on their continued sport participation and their chances for attaining athletic success (Côté, Baker, & Abernethy, 2007; MacDonald, Cheung, Côté, & Abernethy, 2009). The purpose of the present study was to investigate two of these contextual factors, birth date (known as the relative age effect; RAE) and city of development, as determinants of participation in a sample of youth ice hockey players. The sample included 146,424 athletes registered with Ontario youth ice hockey between the 2004-2010 seasons. Chi-square statistics determined a significant RAE in youth ice hockey (?2(3, 146,424) = 1000.34, p < .01, w= .08). Findings also revealed that smaller cities (populations below 100,000) produced significantly more youth ice hockey participants than expected, while larger cities (populations above 100,000) produced significantly fewer youth ice hockey participants than expected. Finally, there was no evidence of an interaction between relative age and city of development. The consistent pattern of RAEs and the characteristics of smaller communities that may facilitate sport participation across all youth are discussed, along with recommendations for future research.Acknowledgments: The authors would like to thank Bill Pearce, Jeffrey Moon, Melissa Wolk, and the OHF for their assistance with this project. Preparation of this manuscript was supported by the Social Sciences and Humanities Research Council of Canada (SSHRC) standard research grant (#410-2011-0472).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".