It’s cold and there’s something to do: The changing geography of Canadian National Hockey League players’ hometowns
Bibliographic record
Abstract
Set within the framework of the birthplace effect literature and the seminal work of Curtis and Birch, this paper draws information from the publicly available database www.hockeydb.com and from the Census to examine the hometowns of Canadian National Hockey League (NHL) players from 1970 to 2015. It found that from a regional perspective, the distribution of players’ hometowns remained fairly stable over the 46-year period with Ontario and the three Prairie provinces being prominent. Players from small centres have been well represented in the NHL. While larger urban areas have historically produced the most players, there has been a marked increase in ‘big city’ players while the odds of making it are low. However, when the analysis is adjusted according to the population aged 10-19, boys growing up in small and mid-sized centres were at advantage in reaching the NHL until 2009. Finally, we discuss whether the growing presence of big city players in the NHL will affect the image of hockey as a national sport, as for many, small-town hockey remains at the heart of Canadian sporting culture.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".