Substitute foods are more likely than their traditional food counterparts to display front-of-package references
Bibliographic record
Abstract
Innovative, highly processed foods are often designed to “substitute” for traditional, less-processed items in the diet. Yet, concerns about the unhealthfulness of diets high in highly processed foods are growing. Their dominance in the diet has been hypothesized to relate, in part, to the strategic use of on-package nutrition promotion. Our goal was to compare front-of-package (FOP) labelling on highly processed products that appear to have been explicitly designed as substitutes for traditional foods with the FOP labelling on their traditional counterparts. FOP references were recorded from packaged foods in three major Toronto grocery stores ( N = 20520). Foods were categorized as substitute or traditional counterparts if these had (1) immediate interchangeability within the diet, (2) inherently different formulation, and (3) the substitute was more heavily processed than its traditional counterpart. Eight substitute–traditional pairs were identified, comprising 18% of products in the data set. Substitute foods were more likely than traditional products to bear FOP nutrition, “organic”, and “natural” references. Substitute foods bore 1.21 times more FOP references, the majority of which highlighted nutrients inherent to the traditional counterpart. Our findings support the contention that highly processed foods may be displacing less-processed foods at least in part through the use of strategic on-package 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".