Prevalence of Child-Directed Marketing on Breakfast Cereal Packages before and after Chile’s Food Marketing Law: A Pre- and Post-Quantitative Content Analysis
Bibliographic record
Abstract
Food marketing has been identified as a contributing factor in childhood obesity, prompting global health organizations to recommend restrictions on unhealthy food marketing to children. Chile has responded to this recommendation with a restriction on child-directed marketing for products that exceed certain regulation-defined thresholds in sugars, saturated fats, sodium, or calories. Child-directed strategies are allowed for products that do not exceed these thresholds. To evaluate changes in marketing due to this restriction, we examined differences in the use of child-directed strategies on breakfast cereal packages that exceeded the defined thresholds vs. those that did not exceed the thresholds before (n = 168) and after (n = 153) the restriction was implemented. Photographs of cereal packages were taken from top supermarket chains in Santiago. Photographed cereals were classified as “high-in” if they exceeded any nutrient threshold described in the regulation. We found that the percentage of all cereal packages using child-directed strategies before implementation (36%) was significantly lower after implementation (21%), p < 0.05. This overall decrease is due to the decrease we found in the percentage of “high-in” cereals using child-directed strategies after implementation (43% before implementation, 15% after implementation), p < 0.05. In contrast, a greater percentage of packages that did not qualify as “high-in” used child-directed strategies after implementation (30%) compared with before implementation (8%), p < 0.05. The results suggest that the Chilean food marketing regulation can be effective at reducing the use of child-directed marketing for unhealthy food products.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".