Did the sweetness of beverages change with the Chilean Food Labeling and Marketing Law? A before and after study
Bibliographic record
Abstract
There has been a rapid proliferation of policies around the globe to reduce sugar consumption, yet there is little understanding as to whether these policies have led to changes in the overall sweetness of products, which is essential for understanding long-term effects on food preferences and intake. For example, the implementation of Chile’s Law on Food Labeling and Advertising led to decreases in the sugar content of non-alcoholic packaged beverages and increases in non-nutritive sweeteners (NNS) use in these beverages. Given that NNS have greater sweetness intensity than sugars, it is unclear what was the net effect of these changes on the sweetness of purchased beverages. Using longitudinal household purchase data ( n > 2,000 households), we measured the changes in the sweetness of beverage purchases after implementing the first phase of the Chilean law and examine if there were differences by key family sociodemographic variables. We developed three sweetness indices: (1) NNS sweetness, including the sweetness of the six NNS most consumed by Chileans; (2) total sugars sweetness, including the sweetness from total sugars; and (3) total sweetness, combining the sweetness from NNS and sugars. Using fixed-effects models, we compared the observed post-law purchases to a counterfactual based on pre-law trends. We found that NNS sweetness increased relative to the counterfactual, while total sugars sweetness decreased after the law. However, the absolute changes in NNS sweetness were almost entirely offset by the decreases in total sugar sweetness, leading to no change in the total sweetness of beverage purchases. Additionally, there were no differences in the sweetness changes by family sociodemographics. Our findings indicate that Chilean consumers are exposed to similar sweetness levels in their beverages after the law. Future research should explore whether sweetness also remained consistent in dietary intake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".