Adaptation in adversity: innovative approaches to food security amidst COVID-19 in a remote First Nations community in Canada
Bibliographic record
Abstract
The COVID-19 pandemic exacerbated food insecurity issues in geographically isolated communities, including Fort Albany First Nation (FAFN). This research examines FAFN's adaptive strategies to improve food security, highlighting community resilience and leadership. Data were gathered through semi-structured interviews with 20 community members who were involved in the pandemic response, either as members of the pandemic committee or as managers of community programs. Thematic analysis revealed significant adaptation of existing programs and the establishment of new initiatives to address food security during the pandemic. Initiatives, such as the community garden and the Fort Albany Farmers Market were food security programs that existed prior to the pandemic, and despite labor shortages during the pandemic, logistical hurdles were addressed to maintain operations and enhance food distribution efficiency. New emergency food initiatives, backed by government support and community efforts, successfully delivered food to vulnerable households. Traditional subsistence activities, such as hunting and fishing, were essential for providing sustenance and strengthening community resilience. These adaptive strategies highlight the critical role of local leadership, community participation and ingenuity, and the utilization of Indigenous knowledge in overcoming food security challenges during crises. This paper underscores the need to support Indigenous food sovereignty and build resilient local food systems tailored to the unique needs of First Nations communities. The experiences of FAFN during the COVID-19 pandemic provide invaluable insights into the resilience and innovation required to improve food security in remote and vulnerable populations, emphasizing the need for sustained investment and policy support in these communities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.014 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".