The community size effect in Canadian Olympic and paralympic athletes: Exploring inter-provincial heterogeneity in athlete development
Bibliographic record
Abstract
Community size effects indicate elite athletes are more likely to originate from regions of medium-to-large population sizes, and less likely to originate from exceedingly small or large cities (Cote et al., 2006; Baker et al., 2009). However, recent literature has revealed variation in this effect both within population size categories (Farah et al., 2017) and between provincial regions of Canada (Wattie et al., 2018). This research explored the consistency of this effect in Canadian Olympians (N = 568) and Paralympians (N = 199) who debuted between 2010 and 2016. Athletes' places of birth were grouped by population size categories: < 2,500; 2,500 – 4,999; 5,000 – 9,999; 10,000 – 29,999; 30,000 – 99,999; 100,000 – 249,999; 250,000 – 499,999; 500,000 – 999999; ? 1,000,000 and the percentage of athletes originating from each population size category were calculated and compared across Canadian provinces. Results showed considerable inter-provincial heterogeneity in athlete development. For instance, the percentage of Olympians in each population size category ranged considerably depending on provincial region: (1.2 - 16.1%), (0 – 3.6%), (2 – 12.5%), (3.6 – 14.4%), (1.8 – 38.5%), (0 – 32.1%), (0 – 50%), (0 – 71.4%), and (0 – 32.4%), respectively. Similarly, the percentage of Paralympians ranged from (0 – 7.1%), (0 – 12.5%), (0 – 7.3%), (0 – 24.4%), (8.2 – 50%), (0 – 57.1%), (0 – 12.5%), (0 – 72.7%), and (0 – 19.5%), respectively. These findings suggest athlete development environments are not equal in similarly sized cities across provincial regions of Canada. Consequently, future research may benefit from exploring specific environmental contexts that are conducive for athlete development that extend beyond population 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".