Harnessing food system equity from the ground up: shifting co-governance practices in the funding of food security responses during the pandemic crisis in Toronto, Canada
Bibliographic record
Abstract
The COVID-19 pandemic was a disrupting force that magnified social inequities and service gaps in underserved urban communities. It was also a “window of opportunity” for the Black Lives Matter movement and Indigenous reconciliation synergies to spur calls to action for more open and inclusive dialog regarding community food security. Increasingly, community-based organizations (CBOs) that have not been traditionally food-focused are becoming more involved in food security responses. These factors have offered space to revisit antiquated and exclusionary practices within resource allocation and decision-making processes that reinforce systems of oppression within the food system. We explore the interconnection between CBOs, municipal actors, and funders in Toronto and draw upon the concept of co-governance to unpack their evolving relationships and influence on equity-focused change in policies and practices. Based on an analysis of interviews (n = 48), this paper articulates that a number of realized progressive, yet incremental, changes have been made, including changes to policies and internal practices and targeted support for Black and Indigenous communities. However, ultimately, a transfer of resources and influence is required in order to achieve the broader goal of harnessing food system equity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".