Effects of community population density and distance to Canadian Hockey League teams on the production of National Hockey League draftees in Canadian provinces
Bibliographic record
Abstract
Recent research has observed significant effects of a community's population density on the production of elite athletes (Rossing et al., 2015; Hancock et al., 2017). A community's proximity to local high performance developmental sport clubs may also favorably expose youth athletes to scouting, resources, and socio-cultural environments that promote athlete development (Curtis & Birch, 1987; Balish & Côté, 2014). However, the effects of population density and proximity to developmental sport clubs on developing National Hockey League (NHL) draftees has yet to be explored. The purpose of this study was to explore the relationship between the number of NHL draftees produced and city/town population density as well as proximity to Canadian Hockey League (CHL) teams in Canadian born hockey players (from all provinces) drafted into the NHL between 2000-2014 (N = 1502). Linear regression analyses showed a significant positive relationship between population density and the production of draftees in all provincial regions (R2 range: 0.019 to 0.229; standardized ?-coefficients range: 0.142 to 0.480). A significant negative relationship between distance to CHL teams and NHL draftee production was observed in 4/6 provincial regions (R2 range: 0.004 to 0.022; standardized ?-coefficients range: -0.066 to -1.570); cities closer to CHL teams produced more athletes. Future research may benefit from exploring the influence of these two variables with respect to inconsistencies in the relationship between a region's population size (i.e., community size effect) and the production of elite athletes (e.g., Baker et al., 2009; Farah et al., 2016).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".