Industry strategies in the parliamentary process of adopting a sugar-sweetened beverage tax in South Africa: a systematic mapping
Bibliographic record
Abstract
BACKGROUND: In 2016, the South African government became the first in the African region to announce the introduction of an SSB tax based on sugar content as a public health measure to reduce obesity. This tax was introduced against the backdrop of South Africa having a large sugar production and SSB manufacturing industry, as well as very high unemployment rates. The introduction of fiscal measures, such as a SSB tax, has been met with well-coordinated and funded opposition in other countries. METHODS: The aim of this study is to describe and analyse the arguments and strategies utilised by industry during policymaking processes to oppose regulatory actions in LMIC. This study analyses arguments and strategies used by the beverage and related industries during the public consultation phase of the process to adopt the South African SSB tax. RESULTS: Industry opposition to the SSB tax was comprehensive and employed several tactics. First, industry underscored its economic importance and the potential job losses and other economic harms that may arise from the tax. This argument was well-received by policymakers, and similar to industry tactics employed in other middle income countries like Mexico. Second, industry discussed self-regulation and voluntary measures as a form of policy substitution, which mirrors industry responses in the US, the Caribbean and Latin America. Third, industry misused or disputed evidence to undermine the perceived efficacy of the tax. Finally, considerations for small business and their ability to compete with multi-national corporations were a unique feature of industry response. CONCLUSIONS: Industry opposition followed both general trends, and also introduced nuanced and context-specific arguments. The industry response experienced in South Africa can be instructive for other countries contemplating the introduction of similar measures.
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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 | 0.021 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".