Preparing Our Home by reclaiming resilience
Bibliographic record
Abstract
Indigenous communities in Canada are faced with a disproportionate risk of disasters and climate change (CIER, 2008). Indigenous communities in Canada are also at the forefront of climate change adaptation and resilience solutions. One program in Canada that aids in decolonizing curriculum for reclaiming resilience in Indigenous communities is Preparing Our Home (POH). Drawing on three POH case studies, this article seeks to answer the following question: How can community-led decolonial educational processes help reclaim Indigenous youth and community resilience? The three communities that held POH workshops, which this article draws upon, include: The Líľwat Nation, where Canada’s first youth-led community-based POH Home curriculum was developed at the Xet̓ólacw Community School; The Siksika Nation, where the workshop engaged youth with experienced instructors and Elders to enhance culturally informed community preparedness through actionable outcomes by developing a curriculum that focused on hazard identification, First Aid, and traditional food preservation; and Akwesasne Mohawk Nation, where political leaders, community members, and community emergency personnel gathered together to discuss emergency preparedness, hazard awareness and ways to rediscover resilience. The participants shared their lived experiences, stories, and knowledge to explore community strengths and weaknesses and community reaction and resilience. The article concludes with a discussion section, key lessons learned in these communities, and recommendations for developing Indigenous community-led curricula. These recommendations include the importance of Indigenous Knowledge, intergenerational learning, land-based learning, participatory methodologies, and the role of traditional language for community resilience. We contribute to the Indigenous education literature by providing specific examples of community-owned curricula that move beyond decolonial education to Indigenous knowledges and experiences sharing, owned by the people and led by the community.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".