Creating a municipal-level emergency food plan: Lessons from Thunder Bay, Ontario
Bibliographic record
Abstract
Emergency food planning is an emerging field of study and practice evolving from lessons learned about the need to be prepared to respond to increased food insecurity in the face of emergency events. In this era of climate change, geo-political conflicts, and growing inequality, disruptions to the global food system are occurring more frequently. Many of these disruptions have the potential to impact food access on a large scale, a reality that communities need to be ready for through preparation to mitigate impacts. Like other municipalities around the world, the city of Thunder Bay in Northwestern Ontario, Canada, and its surrounding areas were caught unprepared by the impacts of the COVID-19 pandemic on food insecurity. Prior to the pandemic, there was no coordinated body to address a sudden increase in food insecurity, particularly among already vulnerabilized populations. In late 2020, Thunder Bay + Area Food Strategy (TBAFS), the regional food policy council, led the coordination of emergency food response and researched the early emergency food response that occurred during the COVID-19 pandemic. Findings from this research identified the need for a collaborative Emergency Food Plan that brought together the municipality and a range of civil society organizations, institutions, and agencies. Acting on this research, the TBAFS coordinated the development of an Emergency Food Plan for the region, leveraging a group of primary partners who make up key components of civil society’s food access infrastructure. This article provides an overview of this process in the context of existing research and literature along with lessons learned throughout the process.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".