Household food wasting in a net‐zero energy neighbourhood: Analyzing relationships between household food waste and pro‐environmentalism
Bibliographic record
Abstract
Abstract To address the prominent “value‐action gap” within pro‐environmental behaviour, this novel, cross‐sectional study investigated relationships between household food wasting and pro‐environmentalism. Research was undertaken in 11 neighbourhoods across London, Ontario, Canada, including a net‐zero energy neighbourhood. A direct measurement methodology was used to measure household food waste, and a survey was created to measure knowledge, attitudes, and behaviours related to food wasting. Households in the net‐zero energy neighbourhood sent between 2.59 kg and 2.80 kg of food waste to landfill per week, of which 68% was classified as avoidable and the remaining 32% as unavoidable. Households in this neighbourhood sent less total (p < 0.001) and unavoidable (p < 0.001) food waste to landfill than households in “regular” neighbourhoods within the same city. While participants in the net‐zero neighbourhood had strong, self‐reported pro‐environmental worldviews, pro‐environmentalism was not found to be stronger in this neighbourhood compared to the rest of the city. The presence of stronger, self‐reported pro‐environmental worldviews was associated with a decrease in unavoidable food waste generation (p < 0.01). As the first study of its kind, further research is needed to verify the role of pro‐environmentalism in household food wasting in Canada and beyond.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".