Unpacking policy formulation and industry influence: the case of the draft control of marketing of alcoholic beverages bill in South Africa
Bibliographic record
Abstract
Alcohol is a major contributor to the Non-Communicable Disease burden in South Africa. In 2000, 7.1% of all deaths and 7% of total disability-adjusted life years were ascribed to alcohol-related harm in the country. Regulations proposed to restrict alcohol advertising in South Africa present an evidence-based upstream intervention. Research on policy formulation in low- and middle-income countries is limited. This study aims to describe and explore the policy formulation process of the 2013 draft Control of Marketing of Alcoholic Beverages Bill in South Africa between March 2011 and May 2017. Recognising the centrality of affected actors in policy-making processes, the study focused on the alcohol industry as a central actor affected by the policy, to understand how they-together with other actors-may influence the policy formulation process. A qualitative case study approach was used, involving a stakeholder mapping, 10 in-depth interviews, and review of approximately 240 documents. A policy formulation conceptual framework was successfully applied as a lens to describe a complex policy formulation process. Key factors shaping policy formulation included: (1) competing and shared values-different stakeholders promote conflicting ideals for policymaking; (2) inter-department jostling-different government departments seek to protect their own functions, hindering policy development; (3) stakeholder consultation in democratic policymaking-policy formulation requires consultations even with those opposed to regulation and (4) battle for evidence-evidence is used strategically by all parties to shape perceptions and leverage positions. This research (1) contributes to building an integrated body of knowledge on policy formulation in low- and middle-income countries; (2) shows that achieving policy coherence across government departments poses a major challenge to achieving effective health policy formulation and (3) shows that networks of actors with commercial and financial interests use diverse strategies to influence policy formulation processes to avoid regulation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".