Sugar and health in South Africa: Potential challenges to leveraging policy change
Bibliographic record
Abstract
A growing body of evidence indicates that excessive sugar consumption is driving epidemics of obesity and related non-communicable diseases (NCDs) around the world. South Africa (SA), a major consumer of sugar, is also the third most obese country in Africa, and 40% of all deaths in the country result from NCDs. A number of fiscal, regulatory, and legislative levers could reduce sugar consumption in SA. This paper focuses on a sugar-sweetened beverage (SSB) tax. The purpose of the paper is to highlight the challenges that government might anticipate. Policies cannot be enacted in a vacuum and discussion is focused on the industrial, economic, and societal context. The affected industry actors have been part of the SA economy for over a century and remain influential. To deflect attention, the sugar industry can be expected either to advocate for self-regulation or to promote public-private partnerships. This paper cautions against both approaches as evidence suggests that they will be ineffective in curbing the negative health impacts caused by excessive sugar consumption. In summary, policy needs to be introduced with a political strategy sensitive to the various interests at stake. In particular, the sugar industry can be expected to be resistant to the introduction of any type of tax on SSBs.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".