A Direct Measurement Approach to Understanding Influences to Household Food Wasting
Bibliographic record
Abstract
In response to the environmental, economic, and social impacts of wasting food, the United Nations’ Sustainable Development Goal 12.3 aims to halve per capita global food waste by 2030. Aligned with this goal, the overarching research question of this dissertation is: how do pandemic circumstances; a knowledge-based, food waste reduction intervention; and pro-environmental knowledge, attitudes, and behaviours influence the quantity and composition of household food waste generation?\nA key component of this research was to follow a direct food waste measurement methodology, where curbside waste samples from households in London, Ontario, Canada were collected, weighed, and sorted to determine the quantity and composition of wasted food. Additionally, this research used a survey to measure knowledge, attitudes, and behaviours related to household food wasting.\nDuring COVID-19, households sent 2.81 kg of food waste to landfill per week, of which 52% was classified as avoidable food waste and 48% as unavoidable food waste. The generation of unavoidable food waste increased by 65% during the pandemic. These findings can be leveraged to influence policy aimed at developing sustainable solutions for waste management.\nTo address the need for policies and programs that reduce household food waste, the long-term effectiveness of a household food waste reduction intervention was evaluated. Results indicate that the intervention has led to a long-term, sustained 30% reduction in avoidable food waste sent to landfill, demonstrating the potential for the intervention to continue to have a meaningful impact. As one of the only studies to measure the long-term effectiveness of a household food waste reduction intervention, this research fills a gap in our current understanding of intervention efficacy.\nKnowledge of how pro-environmentalism influences household food wasting contributes to strengthening our understanding of the complex, intersecting factors that result in wasted food. Households in a net-zero energy neighbourhood sent less total and unavoidable food waste to landfill than households in ‘regular’ neighbourhoods. While net-zero energy neighbourhood participants had strong, self-reported pro-environmental worldviews, pro-environmentalism was not found to be stronger in this neighbourhood than others in the city.\nOverall, this research contributes to our understanding of household food waste generation and the development of household food waste reduction strategies.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".