Community size effects in Canadian National Hockey League draftees: exploring regional variations in community size effects and the influence of population density and proximity to Canadian hockey league teams.
Bibliographic record
Abstract
Researcher have identified certain population size categories to be more advantageous in developing elite athletes than others (Côté et al., 2006), giving this phenomenon the name “community size effect”. However, inter-categorical comparisons assume uniformity of athlete production within categories, which may be misleading. The purpose of Study 1 was to explore the homogeneity in Canadian National Hockey League (NHL) draftee production within population size categories across provincial regions of Canada. Results showed substantial intra-categorical variability in NHL draftee production, suggesting athlete development environments are not equal between similarly sized cities. The purpose of Study 2 was to explore the effects of population density and distance to Canadian Hockey League (CHL) teams on developing Canadian NHL draftees. Results showed a significant positive relationship between population density and draftee production in all provincial regions, and a significant negative relationship between distance to CHL teams and draftee production in 4/6 provincial regions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".