Child and non-child-targeted food and beverage advertisements on child television in two policy environments in Canada
Bibliographic record
Abstract
While the impact of child-targeted unhealthy food advertising is well-established, children are also exposed to non-child-targeted advertisements. Studying the frequency and effects of both is essential to fully understand advertising's influence on children's health. This study aimed to analyze the frequency of non-child-targeted versus child-targeted food and beverage advertising on children's television stations in two different policy environments in Canada: Ontario and Quebec. This cross-sectional study analyzed advertisements on popular children's television channels in Ontario (hereafter referred to as "English stations") and Quebec (hereafter referred to as "French stations"), recording 648 h of programming across six days in November 2022. The study classified food and beverage products as "of concern from an advertising perspective" based on Health Canada's suggested protocol for classifying food products according to their fat, sugar, and sodium levels. Descriptive statistics were tabulated for the frequency of advertisements and marketing techniques classified as child-targeted and non-child-targeted. The study observed 34,351 ads, with food and beverage ads comprising a larger share on French (25.7 %) than English (11.2 %) stations. Child-targeted ads were more prevalent on English (57.9 %) than French (25.9 %) stations, with child-targeted marketing techniques being notably more frequent on English than on French stations. Advertisements for foods classified as "of concern from an advertising perspective" from a nutritional perspective were highly prevalent on both English (98.9 %) and French stations (88.8 %). These findings suggest that neither self-regulation in Ontario nor statutory regulation in Quebec is sufficiently limiting advertising to children, specifically of unhealthy food and beverage advertising.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".