Grass-roots Innovation for Justice in Urban Food Provisioning
Bibliographic record
Abstract
This paper draws on an international research collaboration to examine grass-roots innovations in food provisioning in five urban locations. These include affordable food projects, comprising social supermarkets and a vegetable box delivery scheme, in Brighton & Hove, UK; an affordable food project targeting black communities in Toronto, Canada; a food fund piloted in Montpellier, France; Solidarity Kitchens in São Paulo, Brazil; and primary school gardens in Cape Town, South Africa. The paper examines the innovative features of these experiences, their comparison to conventional food banks, and their transformative impact on existing food aid narratives and practices. Grass-roots food provisioning models gained traction during the Covid-19 pandemic to address increased food insecurity and offer dignified, culturally appropriate support amid lockdowns. The challenges encountered by these innovations comprise their dependence on surplus food, logistical problems, funding limitations, and the requirement for ongoing volunteer support. Transformative aspects include their empowerment of marginalised groups, fostering of community alliances, and shifting of perspectives on food insecurity, aiming to create sustainable and just local food systems. The paper concludes by emphasising the need for funding to consolidate innovations as well as collaboration between researchers and practitioners to constructively scrutinise innovations and build knowledge on lived experience of food insecurity and injustice. International comparison and learning can enhance capacities, methodologies, and help develop justice-informed alternatives to the prevailing food aid model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| 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".