Co-production Through Volunteerism in Emergency Management: Drawing Lessons from Canada’s Syrian Refugee Resettlement Initiative
Bibliographic record
Abstract
The field of emergency management has been increasingly encouraging the notion of emergency management as a shared, co-productive responsibility, with all members of the society having a role to play. In such whole-of-society efforts, volunteers play a direct role in the co-production of response outcomes. Canada’s mass resettlement of Syrian refugees in 2015 is a case in point, as Canadians rallied en masse to ensure the successful resettlement of thousands of Syrian refugees. In exploring the role of volunteers in this co-productive initiative, there are two important lessons for those in emergency management: The first involves learning from the volunteer management strategies implemented by resettlement agencies, which are applicable for any responding entity tasked with managing whole-of-society response efforts. The second (and perhaps more important) lesson is that those managing whole-of-society response efforts must recognize that value is co-created through three key relationships, a triad between volunteers, response entities, and those directly impacted by a disaster. Each of these relationships must be better understood and managed in order to achieve more effective emergency response outcomes in whole-of-society initiatives.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.020 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| 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".