An examination of sugar-sweetened beverage tax regulations in six jurisdictions: Applying a social justice perspective to beverage taxation and exemptions
Bibliographic record
Abstract
Taxes, legislation and politics are social determinants of health, which can impact health through multiple pathways. The purpose of this study was to review regulations regarding sugar-sweetened beverage (SSB) taxation and describe taxation/exemption of various beverage categories. We reviewed SSB taxation regulations from Mexico, the United Kingdom, Berkeley, Philadelphia, San Francisco and South Africa. Supplementary government documents and academic publications were also reviewed to further discern beverage taxation/exemption and zero-rating. There were a number of beverage types that fell clearly into typically taxed or exempt/zero-rated categories across all six jurisdictions (e.g. pop/soda as taxed and water as zero-rated). Exemptions and ambiguities within the six regulations can generally be grouped as a lack of clarity regarding the meaning and use of milk; the meaning of 'medical purposes' and 'supplemental'; the point at which a beverage is prepared; the form of concentrate (i.e. liquid/frozen/powder) or medium used (e.g. water, coffee); and location of preparation or business size of retailer. SSB tax regulations are complex, unclear, vary across jurisdiction and leave several beverage types with added sugar exempt from taxation or at risk of a legal challenge. Lastly, tax exemptions generally reflect and perpetuate existing sociopolitical dynamics within the food system.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".