Towards a cohesive circular food economy: A motivation opportunity ability (MOA) approach to understanding an emerging group of practitioners in Metro Vancouver
Bibliographic record
Abstract
Nearly half of the food produced in Canada is lost or wasted, leading to negative environmental impacts and contributing to rising levels of food insecurity. The circular food economy (CFE) has been proposed by stakeholders and policymakers as a potential framework for solving the food waste problem through a variety of business and nonprofit food-related waste reduction and prevention initiatives, creating a community-based circular food system. This research asks: How do individuals working in the food sector mobilize CFE practices in their work? What are the motivations, opportunities, and abilities influencing those working in the emerging CFE sector in Metro Vancouver? To answer these questions, this research analyzed interview data from food sector stakeholders (n = 22) contributing to the Metro Vancouver CFE. This study applies the motivation opportunity ability (MOA) framework to guide data analysis. The findings indicate that there are conflicting priorities to CFE approaches in Metro Vancouver, leading to a lack of cohesion among initiatives and to barriers to a more equitable CFE. Stakeholders contributing to the CFE notice competing visions in best practices to reduce waste leading to a paradox of managing waste instead of prevention.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".