Community Resilience and Creating Capacities for Risk Reduction in First Nations Communities, Case Study in Minegoziibe Anishinabe (Pine Creek First Nation)
Bibliographic record
Abstract
The colonization of Indigenous peoples in Canada has serious consequences on First Nations, including forced removal and displacement from their ancestral lands, environmental degradation, declining resources and capacities, and human rights violations. First Nations communities are currently facing the amplified effects of human-driven climate change. Sustainability of the environment is not just a concept, but a practiced way of life, that recognizes the interdependence of all living things. This deep respect for Aki (earth) is at the foundation of First Nations cultures and continues to guide their actions to insure better futures for Seven Generations. The community of Minegoziibe Anishinabe (Pine Creek First Nation), located in Manitoba, has recently confronted life-threatening events of wildfires and floods. Like many First Nations, they have also faced the harmful social effects resulting from the legacy of Indian Residential Schools (IRS) and the epidemic of drug and alcohol use (Bombay et al., 2014). The community is creating capacities for risk reduction through taking care of their mental, physical, emotional, and spiritual well-being. Traditional roles and responsibilities of the Chief and Council, Elders, Knowledge Keepers, and community members have helped guide mechanisms for emergency response and recovery. The analysis uses a holistic approach to understand community resilience (CR) through decolonized frameworks for disaster risk reduction (DRR). Keywords: First Nations, Indigenous, land-based knowledge, Medicine Wheel, community resilience (CR), disaster risk reduction (DRR)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".