Strong advocacy led to successful implementation of smokefree Mexico City
Bibliographic record
Abstract
OBJECTIVE: To describe the approval process and implementation of the 100% smokefree law in Mexico City and a competing federal law between 2007 and 2010. METHODS: Reviewed smokefree legislation, published newspaper articles and interviewed key informants. RESULTS: Strong efforts by tobacco control advocacy groups and key policymakers in Mexico City in 2008 prompted the approval of a 100% smokefree law following the WHO FCTC. As elsewhere, the tobacco industry utilised the hospitality sector to block smokefree legislation, challenged the City law before the Supreme Court and promoted the passage of a federal law that required designated smoking areas. These tactics disrupted implementation of the City law by causing confusion over which law applied in Mexico City. Despite interference, the City law increased public support for 100% smokefree policies and decreased the social acceptability of smoking. In September 2009, the Supreme Court ruled in favour of the City law, giving it the authority to go beyond the federal law to protect the fundamental right of health for all citizens. CONCLUSIONS: Early education and enforcement efforts by tobacco control advocates promoted the City law in 2008 but advocates should still anticipate continuing opposition from the tobacco industry, which will require continued pressure on the government. Advocates should utilise the Supreme Court's ruling to promote 100% smokefree policies outside Mexico City. Strong advocacy for the City law could be used as a model of success throughout Mexico and other Latin American countries.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".