Exploring an Olympic “Legacy”: Sport Participation in Canada before and after the 2010 Vancouver Winter Olympics
Bibliographic record
Abstract
Guided by the notion of a trickle‐down effect, the present study examines whether sport participation in Canada increased following the 2010 Winter Olympics in Vancouver. Comparing rates of sport participation prior to and following the Games using nationally representative data, the results suggest that the Olympics had almost no impact on sport participation in Canada, although there does appear to be a modest “bounce” in sport participation in the Vancouver area immediately following the Vancouver Games. As such, if the trickle‐down effect did occur, the analysis suggests that the effect was locally situated, short‐lived, and small. Inspirée par la notion de l'effet de retombée, la présente étude examine si la participation sportive au Canada a augmenté à la suite des Jeux olympiques d'hiver de 2010 à Vancouver. Les résultats de la comparaison des taux de participation au sport avant et après les Jeux utilisant des données représentatives au niveau national, suggèrent que les Jeux olympiques ont eu pratiquement aucun impact sur la participation sportive au Canada, même s'il semble y avoir un faible “rebond” dans la participation au sport dans la région de Vancouver immédiatement après les Jeux. Cela étant, si l'effet de retombée s'est produit, l'analyse suggère qu'il a été modeste et de courte durée et on l'a ressenti seulement localement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".