The challenge of classifying foods and beverages marketed to children (632.3)
Bibliographic record
Abstract
Banning the marketing of “unhealthy” foods to children has been proposed as a strategy to address the rising rate of childhood obesity. Criteria have been developed to define what constitutes marketing to children; however, we hypothesize that application of these criteria may yield variable results due to the inherent subjectivity of such criteria. The objective of this study was to evaluate inter‐rater variability by nutrition experts in the classification of products as marketed to children. Eight experts independently evaluated the complete package label of 100 foods from a database of Canadian prepackaged products using the following criteria: 1) allusions to fun/play; 2) child‐oriented lettering/graphics; 3) cartoon/children's characters; 4) toys/prizes/contests/coupons; 5) games; 6) unusual flavour/shape/colour of product; and 7) children's product lines. Classification was inconsistent for 55% of products, with the highest levels of disagreement found for criteria 1) allusions to fun/play and 2) child‐oriented lettering/graphics. The highest agreement among raters was for criteria 4) toys/prizes/contests/coupons and 3) cartoon/children's characters; nevertheless, for these types of product marketing, there was unanimous agreement only 25% and 20% of the time, respectively. This subjectivity illustrates potential challenges in implementing recommendations to ban the marketing of “unhealthy” products to children. Grant Funding Source : Supported by McHenry Chair grant (ML), OGS (CM), CIHR Master's (CM), CIHR PICDP (CM)
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 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 | 0.108 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".