Advocacy Coalitions, Policy Entrepreneurs, and Windows of Opportunity: Tobacco Control in South Africa, 1948-2018
Bibliographic record
Abstract
This article examines the political history of tobacco control policy in South Africa from 1948 to 2018 by drawing on available historical documents, media reports, published books and articles, the grey literature, and face-to-face interviews with key policy actors. Tracing the historical evolution of tobacco control policies in South Africa reveals how embedded opposition from vested interest groups at every stage of the policy process complicates responses to the tobacco issue. This case study demonstrates how, despite such embedded difficulties, a confluence of regime change, evidence-based messaging, political will, policy entrepreneurs, and advocacy coalitions have led to the gradual transformation of tobacco control policy in South Africa over time. Understanding the historical evolution of tobacco control policy in South Africa opens up space for an in-depth inquiry that allows researchers to trace the policy-making process over the last seven decades, and to understand how those processes have facilitated a shift in the orientation of policy makers over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".