Tracking Kids’ Food: Comparing the Nutritional Value and Marketing Appeals of Child-Targeted Supermarket Products Over Time
Bibliographic record
Abstract
Marketing unhealthy foods negatively impacts children’s food preferences, dietary habits and health, prompting calls for regulations that will help to create an “enabling” food environment for children. One powerful food marketing technique is product packaging, but little is known about the nature or quality of child-targeted food products over time. This study assesses how child-targeted supermarket foods in Canada have transformed with respect to nutritional profile and types of marketing appeals (that is, the power of such marketing). Products from 2009 (n = 354) and from 2017 (n = 374) were first evaluated and compared in light of two established nutritional criteria, and then compared in terms of marketing techniques on packages. Overall, child-targeted supermarket foods did not improve nutritionally over time: 88% of child-targeted products (across both datasets) would not be permitted to be marketed to children, according to the World Health Organization (WHO) criteria, and sugar levels remained consistently high. Despite this poor nutritional quality, the use of nutrition claims increased significantly over time, as did the use of cartoon characters and appealing fonts to attract children’s attention. Character licensing—using characters from entertainment companies—remained consistent. The findings reveal the critical need to consider packaging as part of the strategy for protecting children from unhealthy food marketing. Given the poor nutritional quality and appealing nature of child-oriented supermarket foods, food product packaging needs to be included in the WHO’s call to improve the restrictions on unhealthy food marketing to children.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".