Female Youth Soccer Participation and Continued Engagement: Associations With Community Size, Community Density, and Relative Age
Bibliographic record
Abstract
Environmental context can impact youth engagement in sport and athlete development. Previous work in this area has examined the population size of the birthplace of elite athletes; commonly known as the birthplace effect. While some consistency has been observed in North American samples, exact estimates for the ideal community size and a thorough understanding of the underlying mechanisms has been somewhat elusive. Existing studies are cross-sectional in nature and there is some evidence to suggest that significant variation exists within imposed community size categories. Thus, the overarching purpose of this study was to establish longitudinal participation trends among female youth soccer players in Ontario Canada, with consideration of both community size and community density. This study also examined whether relative age has any association with community density. Registration entries at age 10 years (n = 9,826) and 16 years (n = 2,305) were isolated for analysis, representing the pre- to post-adolescent transition years. Odds ratio analyses were conducted within each community size and community density category for all 10-year old registrants; 95% confidence intervals were obtained. This procedure was repeated for all registrants at 16 years of age using the expected distribution at age 10 years to examine continued engagement; and, within-category variation was assessed. Findings suggest medium-sized communities (i.e., 10,000-249,999 inhabitants) provide the best odds of participation and continued engagement during the pre- to post-adolescent transition years. Less densely populated communities (i.e., 50-<400 population/km2) appeared to be ideal for facilitating participation at age 10 years, but not for engagement at age 16 years. However, within-category variation was evident when each community was inspected individually. Consistent with previous attempts to find an association between community size and the relative age effect, there did not appear to be a relationship between community density and birth quartile distribution. Observations from this study show that community size and community density are truly unique and separate variables; one does not inform the other. Future studies should consider the underlying contributions to both low and high participation and continued engagement, while being mindful of within-category variation and region-to-region differences between communities of comparable size.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".