Mobilizing Networks and Relationships Through Indigenous Food Sovereignty: The Indigenous Food Circle’s Response to the COVID-19 Pandemic in Northwestern Ontario
Bibliographic record
Abstract
This paper explores the Indigenous Food Circle’s (IFC) response to the COVID-19 pandemic in Northwestern Ontario, Canada. Established in 2016, the IFC is an informal collaborative network of Indigenous-led and Indigenous-serving organizations that aims to support and develop the capacity of Indigenous Peoples to collaboratively address challenges and opportunities facing food systems and to ensure that food-related programming and policy meets the needs of the all communities. Its primary goals are to reduce Indigenous food insecurity, increase food self-determination, and establish meaningful relationships with the settler population through food. This community case study introduces the IFC and shares the strategies and initiatives that were used during the COVID-19 pandemic in 2020 to address immediate needs and maintain a broader focus on Indigenous food sovereignty. The food related impacts of the COVID-19 pandemic on Indigenous People and determining solutions cannot be understood in isolation from settler colonialism and the capitalist food system. Reflecting on the scholarly literature and the experiential learnings that emerged from these efforts, we argue that meaningful and impacting initiatives that aim to address Indigenous food insecurity during an emergency situation must be rooted in a decolonizing framework that centers meaningful relationships and Indigenous leadership.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".