Urban household food waste: drivers and practices in Toronto, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this paper is to understand determinants of food waste through analysing patterns of practices including shopping, planning, consumption of leftovers and attitudes around best-before dates. Design/methodology/approach A survey and waste composition analysis of 142 households was conducted in the City of Toronto. Bivariate analyses and confirmatory factor analysis (CFA) using a structural equation model were used to identify relationships between per capita food waste, household socio-demographic characteristics and household food practices. Findings Constructs related to planning practices and best-before date practices were identified through the CFA. Household size and the best-before construct were negatively correlated with per capita food waste. The planning construct had no correlation, which may be attributed to the influence of the retail environment in encouraging unplanned purchases. The best-before construct was significantly correlated with the presence of children in the home, an indicator of the compromises that parents make in domestic provisioning to ensure healthy foods for their children, such as more caution in handling items after their best-before dates. Originality/value This is the first study of its kind that uses directly measured per capita food waste from a waste composition study in a structural equation model with a construct related to best-before dates to determine drivers of food waste. It is also the first to find that children in the home can have an indirect influence on food waste through the household's best-before practices.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it