Land‐based meanings of disaster adaptations from Woodland Cree First Nation, Canada
Bibliographic record
Abstract
Abstract This paper showcases how climate disaster presents unique challenges to many northern Indigenous communities in Canada. Disasters further exacerbate historical traumas related to colonialism, including forced displacement, land loss, and cultural disconnection. Indigenous cultural barriers and the imposition of external interventions deter effective communication and disrespect Indigenous sovereignty and traditional Indigenous land‐based adaptation. Limited resource allocation prolongs socio‐economic disparities, increasing vulnerability to climate‐related disasters. Western disaster management often overlooks Indigenous worldviews and traditional environmental practices, leading to culturally insensitive and ineffective disaster responses. This paper advances an Indigenous Land‐based Theoretical Framework that centres Indigenous Knowledges, cosmologies, and environmental stewardship in disaster adaptation. This framework offers holistic, culturally relevant disaster education by advocating for communities to draw on their traditional land‐based practices of adaptability and resilience. Through collaborative research with Indigenous and non‐Indigenous land‐based scholars, this paper highlights the importance of Indigenous‐led, land‐based disaster management strategies. These strategies not only address immediate disaster challenges, but also support long‐term sustainability, environmental preservation, and cultural continuity, offering critical insights for effective and culturally sensitive disaster management practices in the context of climate change.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".