OPENING UP THE BOX: EXPLORING THE SCALING OUT OF THE GOOD FOOD BOX ACROSS CANADA
Bibliographic record
Abstract
The Good Food Box (GFB) program holds a great deal of promise to expand our understanding of Community Food Security (CFS). CFS represents a vision for solving hunger and other problems with the food system through an integrated approach that improves access to good and appropriate food for all while at the same time building community, strengthening local agricultural economies, and maximizing social justice. The GFB, one type of CFS program, is a community-based initiative found across Canada that provides a box of healthy food to customers at near wholesale prices; it has the potential to increase access to healthy food, develop alternative distribution channels, link producers more closely with consumers, build community connections, and more. Yet despite the fact that over 50 unique GFB programs exist across Canada, little research has been done on how these myriad programs are structured and function, how this program model has spread to and been adapted by communities across Canada, and how individual programs operate while balancing multiple goals and priorities. This paper, based on qualitative interviews with managers at 21 GFB programs across Canada, explores the diversity of GFB programs in Canada, and how these programs balance multiple priorities along with day-to-day logistical constraints. GFB programs functioning across Canada have diverse goals, tensions sometimes arise when balancing multiple goals, and programs have found various ways to resolve these tensions. Moreover, GFB programs are educating and empowering people in their communities, as well as networking and learning among themselves. This is one of the first studies describing the breadth of GFB programs across Canada, and some of the findings have not been identified in previous scholarship. I describe the variety of program structures, the main priorities and goals that the programs identify, and some of the tensions and innovations that arise when working to balance the multiple goals and dimensions of CFS. I also discuss how programs communicate and learn from each other, and how the GFB in Canada can help us understand the CFS movement more generally.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.039 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".