Canadian citizens volunteering in disasters: From emergence to networked governance
Bibliographic record
Abstract
How to improve coordination between formal and unaffiliated or spontaneous volunteers after emergencies is currently an international question with a high profile. Drawing on international disaster management literature and experiences and recent crisis events in Canada, our analysis examines four Canadian case studies to show that the inclusion of citizens in EM is becoming indispensable, as simultaneously as the frequency and intensity of natural disasters are seen to be growing due to climate change, and citizens are increasingly presenting their labour and resources as assets to be drawn on in emergency and postemergency situations. In this context, Canadian municipalities are starting to better manage the unpredictability of spontaneous citizen volunteering in emergencies by building anticipatory structures of networked governance for integrating diverse, pre‐existing, and in some cases, pre‐identified groups of citizens as volunteers in emergency management functions. Additionally, as the role of voluntary service organizations is becoming elevated in emergency response and recovery in Canada, these organizations can prospectively play the role of brokers to help emergency management agencies access and manage community‐based networks of voluntary resources.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".