Preparing for food security after COVID-19: Strengthening equity and resilience in future emergency response in Toronto
Bibliographic record
Abstract
In early March of 2020, the COVID-19 pandemic emerged as a global health emergency that few governments were prepared to handle. Prior to the outbreak, food insecurity was already a serious public health problem impacting over 4 million Canadians, including 1 in 5 residents (18.5%) of the City of Toronto’s population. The severity of impacts of the emergency crises on vulnerable populations is dependent upon the resiliency of the food system, or the ability of a system to absorb or adapt to ‘shocks’ so that it can continue to function and provide services. This SSHRC-funded collaborative research project brings together scholars from Ryerson University’s Centre for Studies in Food Security and the City of Toronto’s Poverty Reduction Strategy Office. It aims to enhance the existing capacity of the municipal government in assessing how vulnerable neighbourhoods and food security organizations responded to the initial and residual impacts of COVID-19, and bridge gaps in local and expert knowledge necessary for developing an emergency preparedness strategy for future food-system shocks that upholds the City of Toronto’s resilience and equity goals. This project will: Investigate the responses of communities and organizations, including those that emerged in Neighbourhood Improvement Areas (NIAs) to address heightened food insecurity during the outbreak and recovery in the City of Toronto Assess emergency response preparedness in food security practice in other cities to evaluate how equity and resiliency concerns are considered before, during and after the outbreak Broker local and expert knowledge on the impacts of the COVID-19 response on the resiliency and equity of Toronto’s food systems Inform and strengthen food-system practice and policy in future emergency response.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".