The cost of confusion: How label knowledge and risk attitude shape household food waste
Bibliographic record
Abstract
This research explores how consumer knowledge of date labels influences food waste behavior and associated financial loss, emphasizing the role of individual differences in risk taking and environmental values. Based on a survey of 954 participants from a Canadian population, the study finds that while most consumers understand the distinction between best-before and use-by labels, most consumers still rely heavily on date labels, often discarding edible food due to perceived safety concerns. Importantly, the relationship between label knowledge and waste behavior is shaped by food-related risk taking: This study supported that individuals with higher risk-taking tolerance successfully act on their knowledge of date labels to reduce waste, supporting the hypothesis that risk-taking moderates this effect. Meanwhile, environmental values directly predict lower food waste but do not significantly moderate the effect of knowledge, suggesting that pro-environmental consumers may already follow sustainable food management practices independent of label understanding. The model also confirms that household food waste predicts household financial loss, with an estimated average cost of CAD 246 per year attributable to misinterpreting date labels. These findings underscore the complexity of consumer behavior and the need for targeted interventions. Educational campaigns should be tailored to different risk profiles, and label redesigns should incorporate sensory cues to build confidence in food safety assessments.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".