The passage of tobacco control law 174 in Lebanon: reflections on the problem, policies and politics
Bibliographic record
Abstract
Progress in tobacco control policy making has occurred worldwide through advocacy campaigns involving multiple players- civil society groups, activists, academics, media and policymakers. The Framework Convention on Tobacco Control (FCTC)-the first ever global health treaty-outlines evidence-based tobacco control policies. Lebanon ratified the FCTC in 2005, but until 2011, tobacco control policies remained rudimentary and not evidence-based. Beginning in 2009, a concerted advocacy campaign was undertaken by a variety of stakeholders with the aim of accelerating the process of adopting a strong tobacco control policy. The campaign was successful, and Law 174 passed the Lebanese Parliament in August 2011. In this article, we analyse the policy making process that led to the adoption of Law 174 using Kingdon's model. The analysis relies on primary and secondary data sources including historical records of key governmental decisions, documentation of the activities of the concerted advocacy campaign and in-depth interviews with key stakeholders. We describe the opening of a window of opportunity as a result of the alignment of the problem, policy and politics streams. Furthermore, findings revealed that despite the challenge of persistent tobacco industry interference and established power relations between the industry, its allies and policymakers; policy entrepreneurs succeeded in supporting the alignment of the streams, and influencing the passage of the law. Kingdon's multiple stream approach was useful in explaining how tobacco control became an emerging policy issue at the front of the policy agenda in Lebanon.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".