The dilemma of scaling up local food initiatives: Is social infrastructure the essential ingredient?
Bibliographic record
Abstract
The purpose of this paper is to reflect on and compare two responses to the challenge of scaling up local food initiatives. Comparative case studies of the Good Food Box in the City of Edmonton and the Rimbey farmers’ market are used to examine the different strategies used to scale up their impacts as a means of providing a meaningful alternative to the status quo. Our findings suggest that investments in social infrastructure are crucial for maintaining the values and integrity of local food initiatives and also to highlight the challenges of doing so while in competition with the mainstream food system. Our research identifies how social infrastructure investments for local food initiatives can support radical and strategic incremental changes by managing the risk associated with transformative local food activities and provides opportunities for a reflexive approach to scale by identifying the levers and catalysts for broader change to ensure that investments in food system infrastructure are not made merely for the sake of scaling-up. Social infrastructure is identified as critical for building support for, and attention to, opportunities to scale out and develop connections, networks and partnerships for change beyond food.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.034 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".