From fun to fraught: marketing to kids and regulating “risky foods” in Canada
Bibliographic record
Abstract
In an era of industrialized food production, ultra-processed foods, “Big Food” marketing, and growing obesity rates, food has come to be framed as an object of risk – and as an object of regulation. Such reframing has fascinating implications related to issues of responsibility and decision making, especially when it comes to children’s food. This article probes the relationship between representation, regulation and “risky” consumption with respect to children’s food. I examine how child-targeted foods become framed as “risky” and what counts as “risky” food messaging under Health Canada’s commitment to restrict the marketing of unhealthy foods to children. Detailing the tension between food as a risk object and food as a child object, I suggest how issues of semantic provisioning and the politics of the unseen work to complicate and destabilize the (seemingly) straightforward process of prohibiting 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".