Host community logistics and advanced preparation: Insights from wildfire evacuations in Alberta
Bibliographic record
Abstract
This paper examines the experiences of three host communities in Alberta involved in supporting and accommodating evacuees during the record 2023 wildfire season in Canada. Exploring host community service provision is a small but important part of a growing body of research that attempts to understand challenges faced by hosts when evacuations take place. Wildfire displacements can have long-lasting and deep social impacts, and the ability of host communities’ to implement socially aware and holistic emergency management plans and coordinate with key stakeholders is an important area of inquiry. When accommodating evacuees, hosts may face social services strains, uncertainties regarding cost recovery, and traffic and hospitality congestion. As such, this paper deploys case study methods seeking answers to three research questions stemming from the 2023 Alberta Wildfires: 1) How well prepared were communities to host evacuees?; 2) What challenges did they encounter?; and 3) What lessons can be learned from their experiences? Semi-structured interviews (n=27) across the three cases of High Level, Whitecourt, and Hinton revealed important insights regarding community preparedness, community-specific challenges, and cross-cutting lessons learned. Major findings demonstrate the importance of intercommunity coordination, rapid needs assessments of incoming evacuees, planning efforts to provide adequate accommodations, and increased awareness around disaster relief programs and incident command training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".