Vancouver and the 2010 Olympic Games: Physical Activity for All?
Bibliographic record
Abstract
BACKGROUND: The City of Vancouver, British Columbia strategically designed and implemented a municipal health promotion Policy--the Vancouver Active Communities policy--to leverage the 2010 Olympic Games. The goal of the policy was to increase physical activity participation among Vancouver residents by 2010. METHODS: In this paper, we conduct a critical policy analysis of health promotion policy documents that were available on the City of Vancouver's website. RESULTS: We elaborate on the background to the policy and more specifically we examine its content: the problem definition, policy goals, and policy instruments. DISCUSSION: Our analysis showed inconsistency within the policy, particularly because the implemented policy instruments were not designed to address needs of the identified target populations in need of health promotion efforts, which were used to legitimize the approval of funding for the policy. Inconsistency across municipal policies, especially in terms of promoting physical activity among low-income residents, was also problematic. CONCLUSIONS: If other municipalities seek to leverage health promotion funding related to hosting sport mega-events, the programs and services should be designed to benefit the target populations used to justify the funding. Furthermore, municipalities should clearly indicate how funding will be maintained beyond the life expectancy of the mega-event.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".