Seeing the forest but not the trees: Heterogeneity in community size effects in Canadian ice hockey players
Bibliographic record
Abstract
The community size effect (or birthplace effect) suggests that high-performance athletes are less likely to emerge from regions with population sizes that are very small or very large. However, previous research on elite Canadian ice hockey players has not considered the influence of intra-national regional variation of population distributions with respect to community size effects. Therefore, the purpose of the current study was to test the heterogeneity of the community size effect between Canadian National Hockey League draftees (2000-2014: n = 1505), from 7 provincial regions within Canada (i.e., British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec and the Atlantic Provinces). The proportion of athletes in the 9 census population categories were compared to the national and regional general population distributions in the census categories. Results suggest variability of community size effects between the 7 provincial regions within Canada, with only the province of Ontario demonstrating a community size effect congruent with effects reported in previous research. Using regional general population distributions as the comparator to athlete populations changed the direction, meaningfulness and magnitude of community size effects. In conclusion, elite ice hockey player community size effects may not be generalisable to all regions within 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".