Geography of Canadian sporting excellence: Does the location of national training centres influence likelihood of being an elite athlete?
Bibliographic record
Abstract
Significant research to date has been devoted to the effect of geographical factors on becoming an elite athlete. However, given that much of our understanding results from National-level analyses, our knowledge of these effects in terms of regional geographical factors is limited. It is hypothesized that regions of a certain size offer access to sport resources early in an athlete's development that facilitate the opportunity for skill acquisition, including national training centres (NTCs) which contain experienced coaching staff and specialized training facilities. The purpose of the present study was to examine how birthplace and location of NTCs effect the development of elite athletes in Canada. Birth province, birthplace size, and distance to NTC were examined for 2234 Canadian athletes from the 1996 to 2014 Olympic and Paralympic Games and compared to age-matched cohorts from the Canadian census. Results showed that many athletes hailed from Ontario, with a national overrepresentation of athletes from regions of 500,00-999,999. Despite the overrepresentation of athletes who originated from larger regions that housed the majority of NTCs, many athletes, particularly winter Olympic athletes, did not originate from areas within commuting distance or 80 km of the NTC for their sport. These findings continue to emphasize the complexities of geographical factors on the understanding of skill acquisition. In an effort to enquire beyond broad-sweeping National-level analyses, further exploration into regional infrastructure factors including provincial training centres, individual clubs, and other specialized facilities will continue to develop insight into where Canadian elite athletes originate.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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".