Ontario Menu Calorie Labelling Legislation: Consumer Calorie Knowledge Six Months Post-Implementation
Bibliographic record
Abstract
PURPOSE: In the province of Ontario, a new law requires restaurants and food service providers, with more than 20 locations in Ontario, to prominently list the calorie content of their food items on the menu. This study examined if the new calorie information shifted the Ontario consumer's ability to more accurately estimate calories. METHODS: Using an online survey, consumers (n = 665 non-Ontario control and n = 694 Ontario) were asked to estimate the calories of a popular menu item (a cheeseburger) prior to this new legislation and 3 months and 6 months after the introduction of the mandated calorie labels on menus. RESULTS: Early results suggest that one cannot yet see a clear overall change in the Ontario consumer's ability to estimate calories (based on 1 popular food item) since the introduction of mandated calorie labels on menus, although the most recent survey data suggest promise. CONCLUSIONS: Consumers, not just in Ontario, are poor at estimating calories. Repeated exposure to the calorie information now posted on most Ontario fast-food menus is an educational initiative expected to show benefits in the future, but additional time is required for measurable increases in consumer knowledge.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.005 | 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".