Does size of one’s community affect likelihood of being drafted into the NHL? Analysis of 25 years of data
Bibliographic record
Abstract
The consistency of community size effects in North American contexts but not elsewhere, reinforces the notion that the effect is driven by socio-cultural factors specific to the country under examination. In order to identify and understand the various forces driving the community size effect, it is important to determine whether the effect has changed over time. Stability or instability over time would assist researchers in identifying the specific socio-cultural mechanisms driving these effects. This study compared the influence of community size on the likelihood of being drafted into the National Hockey League (NHL) among Canadian ice hockey players drafted to play in the NHL between 1985 and 2009. Although there was some variability over the timespan examined, most notably in communities with between 250,000 and 499,999 inhabitants and over 1,000,000 inhabitants, trends were generally stable over time, suggesting that the socio-cultural mechanisms may have also been relatively stable, although further work is necessary to confirm this assumption.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".