Empowering Communities: MFRC’s Role in Food Sovereignty
Bibliographic record
Abstract
This paper examines how community-led food initiatives in Malvern, a neighbourhood in Scarborough, Toronto, respond to structural barriers through the framework of food sovereignty. The central research questions guiding this study are “how do community-based initiatives, like MFRC, address food insecurity and promote food sovereignty, and what kinds of impact do they have within their communities?” Using a qualitative participatory action research (PAR) approach, the study draws on interviews with MFRC staff and participants to explore how their programs challenge dominant food security and charity-based models. The analysis includes a historical and spatial examination of Scarborough’s postwar urban development. It considers how the 1946 Ontario Planning Act, Metro Toronto’s hierarchical governance model and concession-block infrastructure planning produced fragmented, automobile-dependent suburban neighbourhoods, leaving areas like Malvern with limited walkable access to essential services, including affordable food. While both state and market frameworks often view food as a secondary issue, the work of MFRC shows that grassroots organizations affirm food as central to community well-being, identity and autonomy. This study contributes to the ongoing conversation on food justice by reframing food not as supplemental, but as central to social and spatial justice in marginalized urban regions.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.030 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".