Achieving an equitable circular food economy in Vancouver
Bibliographic record
Abstract
Food loss and waste occur at an alarming rate while many households in the City of Vancouver are food insecure. Set within the context of City of Vancouver’s Zero Waste 2040 long-term strategic plan, the City seeks to promote a circular economy, including through food waste reduction and prevention, better food redistribution, and partaking in awareness campaigns. Food rescue and redistribution is often framed as a win–win solution to avoid throwing unwanted, unmarketable, or surplus foods from the landfill and instead redistributing the food to those who are food insecure. This solution has been framed as a useful tool to promote a circular economy. However, both food waste and food studies scholars have rightly noted that connecting unwanted foods with those who are food insecure to address systemic hunger is not a panacea, nor is it a systemic solution to prevent food waste. Drawing on key informant interviews with agri-food experts across the system (n = 20), this study identified the challenges, opportunities, and the overall vision of the circular food economy and how it is mobilised in the City of Vancouver. It seeks to understand how equity factors into the vision of a circular food economy and its implementation by agri-food and relevant actors. The findings in this study highlight the importance of dynamic governance systems that targets critical points for change including regulation, funding, and capacity building to ensure a circular food economy that considers equity and justice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".