Regulating the marketing practices of the unhealthy food and beverage industry
Bibliographic record
Abstract
Background: Sophisticated and pervasive marketing techniques are used by food and beverage companies to increase the sale and consumption of their products. This marketing impacts children’s preferences, requests, nutrition knowledge and dietary intake. The United Nations calls on governments to implement restrictions on marketing practices but there is policy inertia globally. Methods: Seven studies utilised qualitative methodology. Qualitative data were collected from semi-structured expert interviews and documentary analysis. Three legislative approaches in Chile, Canada and the UK served as case studies. Several frameworks guided the data collection and analysis, including the WellCCat framework, a Public Health Law framework, the health policy triangle framework, and a political economy framework. Data were analysed using directed content analysis and reflexive thematic analysis. Findings: There is a weak World Health Organization directive for Member States to act to protect children from unhealthy food marketing, but an emerging human rights directive. Ninety-four countries claim to have marketing restrictions, but their design quality is poor. Self-regulatory systems do not meet the standards of the Public Health Law framework and their design and interpretation leaves industry unaccountable. Marketing restrictions are technically challenging to design and politically challenging to pass through the policy process. The technical design of three proposed laws contained some elements of best practice design, but the scope of each law had limitations. A political economy analysis of the three cases found the overarching neoliberal institutional paradigm created an environment where any form of government intervention must be justified, evidence-based and no more intrusive on commercial enterprise and the lives of the population than necessary. There are multiple ways legislative design could be strengthened. Conclusions: A paradigm shift is needed away from the prevailing neo-liberalism that dominates the policy landscape. One avenue to temper the impact of this dominant paradigm is to utilise the child rights-based approach. Given the universality of the technical issues, more technical support for policymakers regarding designing legislative responses could further aid in overcoming the policy inertia. Finally, strengthening actor networks to champion and support the policy through the political process could contribute to overcoming policy inertia.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 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".