“It's junk food and chicken nuggets”: Children's perspectives on ‘kids' food’ and the question of food classification
Bibliographic record
Abstract
ABSTRACT Given the expansive nature of children's food, banning the advertising of poorly nutritious products to children only deals with part of the problem. What is missing is an understanding of how child‐oriented food marketing has reconfigured children's broader perceptions of what food means and the kinds of foods that are ‘for them’. Drawing from focus groups conducted across Canada, this article examines the perspectives of 225 children who discussed both ‘kids' food’ and ‘adult food’. The research reveals the broader implications of particular food marketing strategies. When children think of ‘kids' food’, they generally think of junk food, sugar, sugary cereals and the fun shapes and unusual colours characterizing much of contemporary child‐oriented packaged food. When children think of ‘adult food’, they think of fruits, vegetables and meat. In short, ‘adult foods’ are generally the unprocessed fruits, vegetables and meats that all North Americans should be consuming more of, whereas ‘kids' foods’ are associated with processed, high‐sugar, low‐nutrient edibles. The paper further reveals how ‘kids' food’ functions as an object or technology of identification for children enacted through a set of characteristics that the edibles share. Children's classification of food also reveals their savvy awareness that both ‘kids' food’ and ‘adult food’ can contain transgressive elements. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".