Towards effective restriction of unhealthy food marketing to children: unlocking the potential of artificial intelligence
Bibliographic record
Abstract
The World Health Organization recommends that member states enact policies to limit unhealthy food marketing to children. Chile enacted relatively stringent laws that restrict unhealthy food marketing to children in two phases, beginning in 2016. Dillman-Carpentier and colleagues examined the incremental effectiveness of the first and second phases of Chile's policy in limiting children's exposure to unhealthy food marketing on television relative to pre-policy. Banning advertisements for all 'high-in' products (i.e., those that exceeded thresholds for energy, saturated fats, sugars and/or sodium) during the daytime (phase 2) was more effective in reducing children's exposure to unhealthy food marketing on television than only banning 'high-in' marketing during programs with large child audiences (phase 1). These findings underscore the importance of implementing comprehensive policies that reduce children's exposure to all marketing for unhealthy foods-not simply that which targets them directly-to better protect them from its negative impacts. However, although policies in Chile and other nations have reduced children's exposure to unhealthy food marketing in broadcast media, it is not clear whether such policies have meaningfully reduced children's overall food marketing exposures. This is partly due to the challenges of studying children's digital food marketing exposures, which are an increasingly important source of unhealthy food marketing. To address these methodologic gaps, several research teams are developing artificial intelligence (AI)-enabled systems to assess food marketing to children on digital media and support efforts to monitor compliance with policies that restrict this marketing. These and other AI systems will be essential to comprehensively and systematically study and monitor food marketing to children on digital media internationally and at scale.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".