Evacuating a First Nation Due to Wildfire Smoke: The Case of Dene Tha’ First Nation
Bibliographic record
Abstract
Abstract Almost every year, First Nations are evacuated in Canada because of wildfire proximity and smoke. Dynamics of wildfires, and remote locations, unique sociocultural characteristics, and limited emergency management resources present challenges for evacuation organizers and residents. This study explores how Dene Tha’ First Nation evacuated their Taché community in July 2012 due to wildfire smoke and how the evacuation process affected evacuees. Interviews were completed with 31 evacuation organizers and residents to examine the factors that helped and hindered the evacuation process. Lack of information about the nearby wildfire, smoke, and evacuation of the nearby small community of Zama City, combined with a generic evacuation plan, delayed and posed challenges during the evacuation of this Dene Tha’ community. Strong leadership and its role in community organizing, keeping families together, providing the social support they needed, and using familiar host communities, demonstrated and contributed to the community’s resilience during the evacuation. Measures to improve evacuations and emergency management in the community and other First Nations in Canada are identified and discussed.
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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 | 0.000 | 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".