A First Nations Framework for Emergency Planning
Bibliographic record
Abstract
In June 2013, a severe flooding of the Bow and Elbow Rivers affected southern Alberta, a province in Canada. The flood was subsequently described to be the costliest natural disaster in Canadian history. Among the hardest hit communities was the Siksika First Nation, located on the Bow River banks about 100 kilometers east of the city of Calgary.A community-university partnership was formed to document the Siksika First Nation community-based response to the health and social effects to their community from the flood. Our qualitative case study sought to: (1) document Siksika First Nation’s response to the health and social impacts resulting from flood in their community; and (2)develop a culturally appropriate framework for disaster and emergency planning in First Nations communities. The Siksika’s work to mitigate the impact of the flood followed a holistic or socio-ecological model that took the determinants of population health into consideration.
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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 | 0.028 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".