Impact of ‘‘high in” front‐of‐package nutrition labeling on food choices: Evidence from a grocery shopping experiment
Bibliographic record
Abstract
Abstract This study investigates the ex‐ante effects of the front‐of‐package (FoP) nutrition labeling for food products high in saturated fat, sugar, and/or sodium, which is a new regulation recently announced by Health Canada to combat obesity. The Canadian food industry has until January 1, 2026, to comply with the new regulations. To examine the ex‐ante effects of this policy, an incentivized experiment is conducted in a lab that replicates a grocery store. The results at the product level indicate a significant decrease in the probability of choosing a product with a “high in” label compared to those without “high in” labels. Basket‐level results demonstrate that FoP labeling is significantly associated with a lower quantity share and dollar value share of products high in one of the mentioned nutrients selected in a grocery basket, as well as fewer grams of sugar and sodium in a grocery basket. Furthermore, the study reveals that individuals with higher educational attainment, a risk‐averse nature, and a lower level of self‐reported nutrition knowledge tend to react more to the labeling. The insights from eye‐tracking data further support these results, revealing that product choices are deterred by a fixation on “high in” labels. This study contributes to an improved understanding of the pathway in which labeling schemes influence food choices.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".