The intersection of structure and agency within charitable community food programs in Toronto, Canada, during the COVID-19 pandemic: cultivating systemic change
Bibliographic record
Abstract
Prior to the COVID–19 outbreak, food insecurity was already a serious public health problem in Canada, impacting 12.7 percent of households. In recent years, activists, practitioners and researchers from a range of health–related disciplines, have debated the legitimacy of food banks and other charitable food programs, contending that policy and programs at the federal level must be prioritized to address the underlying root causes of poverty. This paper challenges the discourse that charitable food programs prevent or distract from Canada’s social equity goals. Alternatively, this paper argues that programs and initiatives at the local level can emerge to bring short–term stability and self–sufficiency to local communities while also advocating for longer–term structural change. Drawing upon structuration theory and critical ecologies of anti–Black racism, we examine the work of BlackFoodToronto, a food sovereignty initiative, to illustrate the negotiation of power and agency, and how groups and networks react to and reshape confining and enabling structures through collaborative practice. In addressing Canada’s food security crisis, this paper offers an alternative perspective of community–based, nonprofit and charitable programs, which in practice, can help inform future food security policy and related health equity and community development strategies.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.045 | 0.033 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| 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".