Unhealthy Food and Beverage Marketing to Children in the Digital Age: Global Research and Policy Challenges and Priorities
Bibliographic record
Abstract
Food and nonalcoholic beverage marketing is implicated in poor diet and obesity in children. The rapid growth and proliferation of digital marketing has resulted in dramatic changes to advertising practices and children's exposure. The constantly evolving and data-driven nature of digital food marketing presents substantial challenges for researchers seeking to quantify the impact on children and for policymakers tasked with designing and implementing restrictive policies. We outline the latest evidence on children's experience of the contemporary digital food marketing ecosystem, conceptual frameworks guiding digital food marketing research, the impact of digital food marketing on dietary outcomes, and the methods used to determine impact, and we consider the key research and policy challenges and priorities for the field. Recent methodological and policy developments represent opportunities to apply novel and innovative solutions to address this complex issue, which could drive meaningful improvements in children's dietary health.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".