How Does the Probability of Purchasing Moderately Sugary Beverages and 100% Fruit Juice Vary Across Sugar Tax Structures?
Bibliographic record
Abstract
OBJECTIVE: Sugar-sweetened beverage taxes are increasingly used to discourage sugar intake; however, the impact on consumer preferences for particular products is largely unknown. This study explored the impact of two tax structures (tiered vs. nontiered and inclusive vs. exclusive of 100% fruit juice) on participants' probability of purchasing moderately sugary beverages and 100% fruit juice. METHODS: A sample of 3,584 Canadians aged 13 years and older completed a series of beverage purchasing tasks, each corresponding to a different tax condition, within an experimental marketplace. Tax conditions included a no-tax control, plus four taxes varying by structure (tiered vs. nontiered) and whether or not 100% fruit juice was included. RESULTS: The odds of purchasing a moderately sugary beverage were higher under tiered versus nontiered taxes. Purchases of higher sugar beverages differed little across tiered versus nontiered structures. Odds of purchasing 100% fruit juice were lower when these products were taxed versus not taxed. CONCLUSIONS: Results suggest that two key tax structures are likely to function as expected; taxes including 100% fruit juice products may lead to lower probability of purchasing fruit juice, and taxes incorporating multiple tiers may be more likely to encourage purchases of moderately sugary products than nontiered formats.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".