Food Waste Prevention and Local Policymaking: A Case Study Analysis of Food Waste Reduction Strategies in the City of Toronto and the City of Vancouver
Bibliographic record
Abstract
<p>Local governments are emerging as critical actors in the implementation of food waste interventions and are increasingly prioritizing the issue on local policy and planning agendas. However, systematic examination of policy instruments and evaluation procedures used by local governments to tackle food waste is lacking, particularly for Canadian cities. This paper presents a case study analysis of food waste strategies implemented by the City of Toronto and City of Vancouver to assess which policy instruments are being used. The findings demonstrate that although cities use a wide array of policy instruments, information-based instruments are the most common and are used to modify consumer behaviour and attitudes towards food waste. The findings also show that determining which policy interventions and instruments are most successful has been obscured by inconsistent or absent evaluation criteria. Accordingly, the potential for municipal policy interventions to be scaled and applied across other urban contexts remains underexploited. </p>
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".