Directly eroding tobacco industry power as a tobacco control strategy: lessons for New Zealand?
Bibliographic record
Abstract
AIMS: To examine some recent examples of tobacco control policies used elsewhere that seek to directly erode tobacco industry power, and to consider the relevance of these to New Zealand. METHODS: A literature search was supplemented with six key informant interviews, with World Health Organization (WHO) officials, and Canadian officials and advocates. RESULTS: The Provincial Government of British Columbia (BC) from 1997 to 2001 had an explicit objective of 'denormalising' the tobacco industry. Legal action was started against the industry to recover healthcare costs. The Canadian Government has been involved in defending its comprehensive tobacco control legislation in court against the industry since 1988. The policies to directly erode industry power, of both Canada overall and at the province level (BC), have been temporally associated with significant declines in smoking prevalence. Since 1998, WHO has conducted a series of inquiries into tobacco industry influence within WHO, and at regional and national levels. Its research and publishing focus on the industry has supported the creation of the Framework Convention on Tobacco Control, which has sections with the potential to assist national governments in strengthening strategies to erode tobacco industry power. The limitations of such strategies, and the uncertainties with using these approaches in the New Zealand context, suggests the need for careful planning and ongoing evaluation. CONCLUSIONS: Recent experience (in several jurisdictions and organisations) suggests that policies to directly erode tobacco industry power may contribute to the effectiveness of comprehensive tobacco control programmes. Some of these lessons could be incorporated into New Zealand's tobacco-control strategy.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".