The portrayal of food marketing policy by Canadian news media
Bibliographic record
Abstract
Unhealthy food marketing influences children's food preferences, intake and rates of obesity.Currently, there is no mandatory national food marketing policy that restricts food marketing to youth in Canada.Little is known about the effects news media may have on the policy process with regard to food marketing.This study aimed to investigate how the Canadian media portrays the issue of food marketing policy, what perspectives are being framed, and who is being quoted.An article search of Canadian news sources on the databases Eureka and Factiva was conducted for the period 1 November 2015 and 1 November 2021.Sixty-five unique news articles on food marketing regulation were identified and a content analysis of each was conducted.The majority of news articles on food marketing regulation framed the topic around health (e.g.obesity, poor dietary intake) and lack of regulation.Food marketing regulation was identified as a solution to the problem in nearly all articles analyzed and was presented positively in 64.6% of articles.Few harms of marketing regulation were identified, while the two main benefits observed were reduced child obesity rates and exposure to food marketing.This study emphasizes the agenda-setting role of news media that were supportive of promoting public health goals.The Canadian media positively promotes government regulation of unhealthy food marketing.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".