Towards equitable & resilient post-pandemic urban food systems: The role of community-based organizations
Bibliographic record
Abstract
In early March of 2020, the COVID-19 pandemic emerged as a global health emergency. Among the many crises that emerged with the onset of the pandemic, COVID-19 has magnified existing weaknesses of global food supply chains and the purchasing power of consumers leading to vulnerabilities in food system resiliency. In Canada and elsewhere, job losses, restricted mobility, and vaccine mandates raise questions about who is capable of or responsible for ensuring food security and food system resilience during times of crises (Béné et al., 2016; O'Hara & Toussaint, 2021). Before the COVID-19 global pandemic, food insecurity was already a severe public health problem in Canada, affecting over 4 million people (Tarasuk & Mitchell, 2020). In Toronto, Canada's largest and most diverse urban region, roughly one in five residents experienced food insecurity pre-pandemic (Tarasuk & Mitchell, 2020). COVID-19 has magnified and further compromised the food security of vulnerable groups, including those living in poverty, those with pre-existing health conditions, the elderly, Indigenous peoples, newcomers, refugees and other racialized minorities (Blay-Palmer et al., 2016; Dachner & Tarasuk, 2017; Gray et al., 2020).
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".