Wapekeka’s COVID-19 Response: A Local Response to a Global Pandemic
Bibliographic record
Abstract
Two years after the onset of the COVID-19 pandemic, many nations and communities continue to grapple with waves of infection and social fallout from pandemic fatigue and frustration. While we are still years away from realizing the full impacts of COVID-19, reflecting on our collective responses has offered some insights into the impact that various public health policies and decisions had on nations' abilities to weather the multifaceted impacts of the pandemic. Widely believed to have the potential to be devastated by COVID-19, many Indigenous communities in Canada were extremely successful in managing outbreaks. This paper outlines one such example, Wapekeka First Nation, and the community's formidable response to the pandemic with a specific focus on food mobilization efforts. Built on over a decade of community-based participatory action research and informed by six interviews with key pandemic leaders in the community, this paper, co-led by two community hunters and band council members, emphasizes the various decisions and initiatives that led to Wapekeka's successful pandemic response. Proactive leadership, along with strong traditional harvesting and processing efforts, helped to take care of the community while they remained strictly isolated from virus exposure.
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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 | 0.000 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".