Population density and proximity to junior developmental teams affect the development of National Hockey League draftees
Bibliographic record
Abstract
Inconsistencies in community size effects found between and within countries (Baker et al Eur J Sport Sci. 2009;9:329-339; Bruner et al J Sports Sci. 2011;29:1337-1344; Wattie et al J Sports Sci. 2018;36:436-444) suggest population size may not be an accurate predictor of athlete development and that other proxies of early environmental characteristics are needed. Researchers have begun to explore the influence of population density and proximity to local sport clubs on athlete development in European countries; however, similar analysis remains to be conducted in Canadian ice hockey. The current study focused on National Hockey League (NHL) draftees and explored whether population density and proximity to Canadian Hockey League teams were associated with the number of draftees produced. Linear regression analyses showed a significant positive relationship between population density and the development of draftees in all provincial regions; however, a significant negative relationship between proximity to CHL teams and NHL draftee development was observed in four out of six provincial regions (British Columbia, Ontario, Quebec, and the Atlantic Provinces). Moreover, population density appeared to be a better predictor of NHL talent development than proximity to CHL teams. Future research may benefit from exploring the effects of these two variables within population size categories, as well as between different regions within provinces.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".