Exploring the impacts of the 2023 wildfire evacuations in the Northwest Territories: a grey literature review
Bibliographic record
Abstract
The Northwest Territories (NWT) experienced an unprecedented wildfire season in the summer of 2023, triggering a record number of evacuation orders across the territory. This review aims to improve understandings of the community-level impacts of the 2023 wildfire evacuations in the NWT, addressing a gap in research by analysing the impacts of the evacuations and offering insights to inform future emergency preparedness and targeted investigations. Using a grey literature review methodology, 96 sources - including news articles, community reports, and government reports were analysed to assess evacuation impacts. Through inductive thematic analysis, nine key themes emerged: mental health; equity-deserving populations; local businesses and economy; evacuee financial struggle; supply chains; healthcare; education; recreation and entertainment; and cross-cutting. While some sources reported that evacuation-related challenges stemmed directly from wildfire threats, many evacuee experiences were intensified by existing gaps in emergency response, communication breakdowns, and inadequate supports for equity-deserving groups. Findings suggest that the impacts of the 2023 NWT wildfire evacuations exacerbated pre-existing vulnerabilities, were marked by communication failures, and had cascading, interconnected, and long-term consequences. This review highlights the far-reaching consequences of wildfire evacuations in the NWT in 2023 and underscores the need for community-led and equity-oriented emergency planning that is responsive to the specific needs of Northern populations.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".