Factors that Contribute to Good Food Box (GFB) Program Sustainability: A Survey of all Known Existing and Discontinued GFB Programs in Canada
Bibliographic record
Abstract
Access to healthy food, including an adequate supply of fresh fruit and vegetables, is a global health and social issue. The methods of accessing and distributing fresh fruit and vegetables has changed over the past several decades, with greater reliance on import and export of goods, changes in farming and agriculture industries and practices, and acknowledgement of the role of poverty and food insecurity issues. Good Food Box (GFB) programs, primarily intended to reach audiences most vulnerable to food insecurity, distribute fresh fruit and vegetables at affordable, lower than regular retail prices to voluntary participants. This paper explores the factors that contribute to sustainability of GFB programs in Canada, using an online survey methodology of all known existing and discontinued GFB programs across Canada. It tests if the factors identified in the literature search do, in fact, contribute to GFB program sustainability in practice in Canada. Case selection was conducted through a review of GFB qualitative research completed in 2013, a general internet search, and snowball sampling of other programs, through a review of publicly available information of those programs and through known programs referring them to the lead researcher. The research study finds that two of the five factors identified in the literature, bricolage and network collaboration, contribute to GFB program sustainability in Canada. Three other factors, policy alignment, Board of Directors governance, and effective performance management were not found to be statistically significant contributors to GFB program sustainability in Canada.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".