The targeting of preschoolers, children, adolescents and adults by the Canadian food and beverage industry on television: a cross-sectional study
Bibliographic record
Abstract
Unhealthy food advertising can negatively impact children's food preferences and nutritional health. In Canada, only companies participating in the self-regulatory Children's Food and Beverage Advertising Initiative (CAI) commit to limiting unhealthy food advertising to children. We analyzed food advertising from 182 Canadian television stations in 2018. A principal component analysis explored patterns of advertising by 497 food companies and their targeting of preschoolers, children, adolescents, and adults. Chi-square analyses tested differences in the volume of advertising between target age groups by heavily advertising food companies and by CAI-participating and non-participating companies. In 2018, Maple Leaf Foods, Boulangerie St-Méthode, Exceldor Foods, Goodfood Market and Sobeys advertised most frequently during preschooler-programming. General Mills, Kellogg's, the Topps Company, Parmalat and Post Foods advertised most frequently during child-programming, while Burger King, McDonald's, General Mills, Kellogg's and Wendy's advertised most frequently during adolescent-programming. CAI-participating companies were responsible for over half of the food advertising broadcast during programs targeted to children (55%), while they accounted for less than half of the food advertising aired during programs targeting preschoolers (24%), adolescents (41%) and adults (42%). Statutory food advertising restrictions are needed to limit food companies' targeting of young people on television in Canada. Novelty: Advertising from fast food restaurant chains dominated television programming targeted to adolescents in 2018. Advertising from breakfast cereal, candy, and snack manufacturers dominated television programming targeted at children in 2018. Over 100 Canadian and transnational companies contravened broadcast restrictions on advertising to preschoolers in 2018.
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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 | 0.000 | 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 | 0.000 | 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".