Sweetener Purchases in Chile before and after Implementing a Policy for Food Labeling, Marketing, and Sales in Schools
Bibliographic record
Abstract
Chile’s landmark food labeling and advertising policy led to major reductions in sugar purchases. However, it is unclear whether this led to increases in the purchases of nonnutritive sweeteners (NNS). The objective of this study was to assess the changes in NNS and caloric-sweetened (CS) products purchased after the law’s first phase. Longitudinal data on food and beverage purchases from 2,381 households collected from January 1, 2015 to December 31, 2017, were linked to nutritional information and categorized into added sweetener groups (unsweetened, NNS-only, CS-only, or NNS with CS). Logistic random-effects models and fixed-effects models were used to compare the percentage of households purchasing products and the mean volume purchased by sweetener category to a counterfactual based on pre-regulation trends. Compared with the counterfactual, the percentage of households purchasing any NNS beverages (NNS-only or NNS with CS) increased by 4.2 percentage points (pp) (95% CI: 2.8, 5.7; P < 0.01). This increase was driven by households purchasing NNS-only beverages (12.1 pp, 95% CI: 10.0, 14.2; P < 0.01). The purchased volume of beverages with any NNS increased by 25.4 mL/person/d (95% CI: 20.1, 30.7; P < 0.01) or 26.5%. Relative to the counterfactual, there were declines of -5.9 pp in households purchasing CS-only beverages (95% CI: −7.0, −4.7; P < 0.01). Regarding the types of sweeteners purchased, we found significant increases in the amounts of sucralose, aspartame, acesulfame K, and steviol glycosides purchased from beverages. Among foods, differences were minimal. The first phase of Chile’s law was associated with an increase in the purchases of beverages containing NNS and decreases in beverages containing CS, but virtually no changes in foods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".