Anticipatory effects of the implementation of the Chilean Law of Food Labeling and Advertising on food and beverage product reformulation
Bibliographic record
Abstract
This study evaluated the anticipated food and beverage product reformulation by industry before the Chilean Law of Food Labeling and Advertising (Law 20.606) was implemented in June 2016 requiring a front-of-package (FOP) warning label for products high in sodium, total sugars, saturated fats, and/or total energy. Fieldworkers photographed a purposive sample of packaged food and beverage products in February 2015 (n = 5421) and February 2016 (n = 5479) from six different supermarkets in Santiago, Chile. The same products collected in both years (n = 2086) from 17 food and beverage categories with added critical nutrients (nutrients of concern: sodium, total sugars, and saturated fats) were included in this longitudinal study. The average change in energy and critical nutrient content was estimated by category. The number of warning labels potentially avoided because of reformulation was determined. Between February 2015 and February 2016, no category experienced reductions >5% average change in energy or critical nutrient content; and some increased in critical nutrient content. Few products (<2%) would have avoided at least one warning label with reformulation. In a diverse sample of food and beverage products, there was minimal reformulation by industry in anticipation of the implementation of the 2016 Chilean Law of Food Labeling and Advertising.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".