Upscaling Collaborative Crisis Management: A Comparison of Wildfire Responder Networks in Canada and Sweden
Bibliographic record
Abstract
One recurrent challenge during major crises and emergencies is how to effectively scale up the response. This involves efforts to expand the number of organizations involved to ensure access to resources, coordination of information and decisions, and joint actions to minimize costs and risks and to restore order. At the same time, upscaling requires difficult decisions about the timing and proper design of crisis responder organizations. In addition, it is generally challenging to orchestrate collaboration among diverse actors from different organizations with different cultures, missions, and experiences. This chapter demonstrates the dynamics of upscaling during two major wildfires in Canada (Fort McMurray, 2016) and Sweden (Västmanland, 2014). We detail the course of events leading up to the activation of joint crisis response organizations and shed light on the formal process of upscaling, how upscaling played out in practice, and how actors perceived performance. The comparison between the two cases demonstrates that although crisis management in Canada and Sweden is organized in different ways, similar challenges emerged in relation to upscaling. We find that differences in perceptions of the situation and divergent beliefs about the sufficiency of local capacities lead to different understandings of the necessity and timing of upscaling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".