Designing legislative responses to restrict children’s exposure to unhealthy food and non-alcoholic beverage marketing: a case study analysis of Chile, Canada and the United Kingdom
Bibliographic record
Abstract
INTRODUCTION: Introducing legislation that restricts companies from exposing children to marketing of unhealthy food and beverage products is both politically and technically difficult. To advance the literature on the technical design of food marketing legislation, and to support governments around the world with legislative development, we aimed to describe the legislative approach from three governments. METHODS: A multiple case study methodology was adopted to describe how three governments approached designing comprehensive food marketing legislation (Chile, Canada and the United Kingdom). A conceptual framework outlining best practice design principles guided our methodological approach to examine how each country designed the technical aspects of their regulatory response, including the regulatory form adopted, the substantive content of the laws, and the implementation and governance mechanisms used. Data from documentary evidence and 15 semi-structured key informant interviews were collected and synthesised using a directed content analysis. RESULTS: All three countries varied in their legislative design and were therefore considered of variable strength regarding the legislative elements used to protect children from unhealthy food marketing. When compared against the conceptual framework, some elements of best practice design were present, particularly relating to the governance of legislative design and implementation, but the scope of each law (or proposed laws) had limitations. These included: the exclusion of brand marketing; not protecting children up to age 18; focusing solely on child-directed marketing instead of all marketing that children are likely to be exposed to; and not allocating sufficient resources to effectively monitor and enforce the laws. The United Kingdom's approach to legislation is the most comprehensive and more likely to meet its regulatory objectives. CONCLUSIONS: Our synthesis and analysis of the technical elements of food marketing laws can support governments around the world as they develop their own food marketing restrictions. An analysis of the three approaches illustrates an evolution in the design of food marketing laws over time, as well as the design strengths offered by a legislative approach. Opportunities remain for strengthening legislative responses to protect children from unhealthy food marketing practices.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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".