Regulation of Food Advertising to Children in Six Jurisdictions: A Framework for Analyzing and Improving the Performance of Regulatory Instruments
Bibliographic record
Abstract
Childhood obesity is a public health crisis, and globally, at least 170 million young people are overweight or obese. Research identifies food marketing as a key risk factor for childhood weight gain, yet there is significant debate over how food marketing to children should be regulated. This paper analyzes regulatory controls on food marketing in six jurisdictions—the United States, United Kingdom, Australia, Ireland, Canada, and Quebec— with the aim of evaluating whether regulation in each jurisdiction exhibits the features of an effective, transparent, and accountable regulatory regime. These jurisdictions use different forms of regulation to restrict food marketing to children (e.g. self-regulation, co-regulation and statutory regulation), yet research suggests that none have been entirely successful in protecting children from exposure to marketing of unhealthy food. Drawing on the disciplines of public health and regulatory studies, we present a theoretical framework for the design of effective food advertising regulation. We use this framework to evaluate the strengths and weaknesses of regulation in each jurisdiction, and to explain why both public and private regulation has been less than successful in improving the food marketing environment. Our analysis reveals significant loopholes in the substantive provisions of regulatory instruments used to restrict food marketing to children, as well as limitations in the processes of monitoring, review, and enforcement established by each scheme. Our paper concludes by pointing to ways in which food advertising regulatory schemes could be progressively strengthened, including through the use of regulatory “scaffolds” to improve the transparency, accountability and performance of regulatory instruments.
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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 | 0.051 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| 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".