Understanding the origins of Canadian Olympic performance: Geographic constraints on the acquisition of sport expertise
Bibliographic record
Abstract
Over the last ten years, there has been significant research devoted to how size of one's birthplace affects likelihood of becoming an elite athlete based on the assumption that access to early resources can impede opportunities for skill development. Despite this attention, our understanding of how geographical factors constrain or facilitate skill development is far from complete. For instance, all prior work in this area has used National level analyses, which may not capture the nuances of development across a nation. In this study, we examined geographical variables among Canadian Olympians to understand their relationship with Olympic athlete development. For this analysis, birth province and size of birthplace were collected for 1144 Canadian summer Olympic athletes and compared to data from age-matched cohorts from the Canadian census. Results indicated significant differences between provinces/territories and birthplace sizes for athletes compared to the general population. More specifically, a) British Columbia had an over-representation of summer Olympic athletes compared to other provinces/territories and b) athletes coming from smaller regions ( 30,000) were significantly over-represented. These findings continue to highlight the significance of geographical factors in understanding sport skill acquisition and athlete development. Furthermore, when considered relative to previous work, our results highlight potential limitations of National-level analyses for understanding these effects and suggest several areas for future work (e.g., the influence of provincial talent pathways and location of national training centres).Acknowledgments: This research was funded by a grant from the Social Sciences and Humanities Research Council of Canada
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".