An Analysis of the Adoption and Implementation of A Sugar-Sweetened Beverage Tax in South Africa: A Multiple Streams Approach
Bibliographic record
Abstract
This paper describes a case study of the adoption and implementation of the sugar-sweetened beverage tax in South Africa, termed the Health Promotion Levy. Qualitative data extraction and analysis of institutional documents, such as policy proposals and parliamentary debate records, stakeholder submissions to Parliament and media reports, were guided by the Kingdon Multiple Streams Theory as adapted to study agenda setting, policy adoption, and implementation. We present the following key findings: First, consistent messaging from policy entrepreneurs, consisting of advocacy groups, health organizations, and research entities, was key to ensuring that a tax policy solution was proposed and passed. Second, the continuity of certain key policymakers contributed to the relatively expedient passage of the tax policy. Third, the use of an excise tax was, amongst others, an appealing policy solution because of its revenue-raising potential; however, uncertainty regarding the purpose of the tax negatively impacted public attitudes toward it. Fourth, industry arguments, relating to unemployment, were effective in restructuring the tax in favor of industry actors. Finally, regulatory action by sectors outside of finance and health impacted stakeholder perceptions of the tax and possibly obstructed regular annual adjustments for inflation.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".