Place of development and dropout in youth ice hockey
Bibliographic record
Abstract
Smaller cities in North America are associated with higher rates of elite talent development in sport compared to larger cities (Côté et al., 2006), but little is known about how city size affects participation. The objectives of this study were to examine the relationship between city size and participation in Canadian male youth ice hockey and to examine if there was a link between city size and dropout using a subset of the sample. A database obtained from the Ontario Hockey Federation provided the participation counts of 15,565 players from 2004 until 2010. To test the relationship between city size and participation, the number of years each player was registered was examined in relation to the city size category (n=9) in which he participated. There was a significant negative correlation (p < .001), meaning participation rates increased as city size decreased. To examine the impact of city size on dropout, the distribution of players who were registered for only 1 or 2 years (n = 1,819) was compared with the distribution of players who were registered for 6 or 7 years (n =11,025) in the 9 city size categories. Odds ratios revealed that cities of over 500,000 people were 2.88 times more likely to produce dropouts than engaged players compared to all other city sizes. Together, these findings suggest that sport programs in smaller cities are more conducive towards promoting prolonged participation in youth sport. Implications for sport program policies will be discussed.Acknowledgments: We would like to thank the Ontario Hockey Federation, Melissa Wolk, Bill Pearce, and Jeff Moon for their great support and contributions towards this project.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".