Educational Campaigns for Product Labels: Evidence from On-Shelf Nutritional Labeling
Bibliographic record
Abstract
Front-of-package and on-shelf nutrition labeling systems in supermarkets have been shown to lead to only modest increases in the purchase of more nutritious foods. Educational campaigns may increase the use of these types of product labels if (1) there is a lack of consumer awareness and/or understanding of the labels, and (2) the information provided leads consumers to prefer different products. The authors study a large-scale national campaign for the Guiding Stars nutrition labels conducted by a grocery retailer in Canada that implemented the labels. Using detailed household transaction data, the authors find only a small increase in the purchase of higher star–rated foods during the campaign, driven by produce purchases, and 60% of the effect disappears after the campaign’s conclusion. Exit surveys were conducted outside of stores before and after the campaign to explain the limited response. Awareness and understanding of the nutrition labeling system increased marginally after the campaign, but there was no increase in self-reported use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.035 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".