Success factors for scaling urban circular businesses in the food sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article investigates success factors for niche circular food businesses to scale up. We first translate Circular Economy thinking to a food systems context by creating a comprehensive overview of circular food activities and measurements. Using Toronto, Canada as a case study, we analyze eleven niche circular food solutions to find success and barrier factors to scale up. Data was collected via questionnaires and interviews, resulting in five categories of factors that either help or hinder circular food business growth. A statistical correlation analysis is performed. The most successful businesses were those that operated in more than one stage of the food chain, had at least 2-3 years to stabilize their performance, and had financial investors. We explore that circular businesses strongly prioritize social and environmental goals and the impact of this when seeking (or avoiding) grants and other traditional business supports. Government and industry partners have a larger role to play in supporting circular businesses but must expand definitions of growth beyond economic metrics to effectively support the transition to a circular food system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it