The birthplace effect in national hockey league draftees: Exploring between city variability trends in athlete development
Bibliographic record
Abstract
The birthplace effect describes the over-representation of high performance athletes from regions with medium-to-large population sizes, and the under-representation of athletes from regions with very small and very large population sizes (Côté et al., 2006). However, it is not clear how homogenous regions are within population categories (< 2,500; 2,500-4,999; 5,000-9,999; 10,000-29,999; 30,000-99,999; 100,000-249,999; 250,000-499,999; 500,000-999,999; >1,000,000) with respect to athlete development. Birthplace data were collected for players drafted into the National Hockey League from 2000-2014 from 6 provincial regions: British Columbia (n=191), Alberta (n=218), Central Provinces (n=218), Ontario (n = 562), Quebec (n = 242), and Atlantic Provinces (n=74). For each provincial region, variability within each population category was explored using mean, standard deviation (SD) and min/max values by comparing the number of athletes that emerged from each city within each population category. Inferential statistics were not run because these were complete populations. Overall, little variability existed within the 4 smallest population categories. Greater variability existed between cities within the larger population categories. For example, in Ontario the five cities within the 250,000-499,999 population category in Ontario produced an average of 20.6 players with a SD of 12.2 and a range of 4 to 37, and the four cities with the 500,000-999,999 population category produced an average of 31.8 players with a SD of 38.2 and a range of 3 to 87. These results reinforce the necessity to study the environmental constraints that make specific regions within population categories more and less conducive to athlete development, and that it may be necessary to reconsider the categorization methodology (i.e., population categories) currently used.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".