Examining the power of the alcohol and tobacco industries in policymaking: Lessons and challenges for the Philippines and Singapore
Bibliographic record
Abstract
Abstract Aims: Transnational alcohol and tobacco corporations are expanding operations in Southeast Asia. This study has two objectives: to examine the power of the tobacco and alcohol industries in shaping tobacco and alcohol policies in the Philippines and Singapore and to identify key lessons and challenges for alcohol and tobacco control. Methods: We developed a conceptual framework from the literature on power and political, commercial and determinants of health. We collected data from official government documents, corporate documents, and news articles for content analysis on the tactics of the alcohol and tobacco industries. We also conducted 30 in-depth, anonymised interviews in the Philippines and Singapore and conducted a thematic analysis of the transcribed interviews. Findings: Transnational and national alcohol and tobacco corporations use various tactics to influence the policy process for alcohol and tobacco control in the Philippines and Singapore. These industries utilised lobbying, litigation or threat of litigation, revolving doors, and marketing to exercise their instrumental power. These industries exercised their structural power by exploiting their market dominance, and public-private partnerships, promoting self-regulation, and benefiting from regulatory capture. They tapped framing tactics, corporate social responsibility activities and public-private partnerships to exert their discursive power. Conclusions: The alcohol and tobacco industries’ exercise of instrumental, structural, and discursive powers is mutually reinforcing. Policymakers, researchers, and civil society organisations working on alcohol and tobacco control need to understand and tackle these powers in the context of power asymmetries within countries and consider the dynamics of local, national, and international laws and corporate practices.
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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 | 0.016 | 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 | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".