Comparing the Canadian front-of-pack labeling regulations with other mandatory approaches in the Americas and their ability to identify ultra-processed products
Bibliographic record
Abstract
ABSTRACT Background Front-of-pack labeling (FOPL) has been implemented in several countries in the Americas, with Chile being the first to introduce a mandatory ‘high in’ warning FOPL in 2016. The Pan American Health Organization (PAHO) food classification criteria, considered a best practice for FOPL regulations, has been adopted by Mexico, Argentina, and Colombia. Canada’s FOPL regulations were recently approved and will take effect in January 2026, but it is unknown how these regulations compare to FOPL regulations that have already been implemented in other parts of the region. Objectives To compare the Canadian criteria for FOPL regulations with other FOPL criteria implemented in the Americas, and to determine their ability to identify ultra-processed products (UPPs). Methods Packaged foods and beverages (n=17,094) from the University of Toronto’s Food Label Information and Price (FLIP) 2017 database were analyzed using three FOPL criteria (Canadian, Chilean and PAHO criteria) and the NOVA classification system. The proportions of products that would be subject to displaying a ‘high in/excess’ FOPL and UPPs that would not be subject to FOPL regulations were examined under each system’s criteria. Agreement patterns were modeled using a nested sequence of hierarchical Poisson log-linear models. The Wald statistics for homogeneity were used to test whether proportional distributions differ significantly. Results Under the Canadian, Chilean and PAHO criteria, 54.4%, 68.4%, and 81.3% of packaged products would be required to display a ‘high in/excess’ FOPL, respectively. Disagreements between the Chilean and the Canadian criteria with PAHO’s were significant, but the greatest disagreement was between the Canadian and PAHO criteria. According to the Canadian, Chilean, and PAHO criteria, 33.4%, 18.4%, 2.3% of UPPs would not be subject to FOPL regulations, respectively. Conclusions A significant proportion of products that should be subject to FOPL regulations according to the PAHO criteria would not be regulated under Chilean and Canadian criteria, resulting in high proportion of UPPs that would not be subject to FOPL regulations. The Canadian FOPL criteria are the most lenient, with the highest proportion of UPPs that would not display a FOPL. Results can inform improvements for FOPL regulations in Canada, Chile and other countries.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".