Factors influencing community recovery decision making : a case study of recovery from the 2016 Fort McMurray wildfires
Bibliographic record
Abstract
When large-scale disasters impact entire communities, entire communities must in turn collectively negotiate the recovery process and the associated recovery decisions. While these decisions affect the recovery outcomes of the community as a whole, they also impact on each of the community’s constituent members. How resources are allocated and which interests are privileged during recovery can directly contribute to the varying recovery outcomes experienced by different members of a community. In this context, the process of deciding who gets what, when and how during disaster recovery becomes especially relevant. With this in mind, the following case study explores the recovery decision-making process in light of recovery from the 2016 Fort McMurray wildfires. The study asks: Which factors influenced whether and how the values, perceptions, needs, and interests of community groups in Fort McMurray were identified, solicited and prioritized in the community recovery decision-making process following the 2016 wildfires? For this purpose, a single-case, exploratory case study was undertaken, with data collected through semi-structured interviews with 16 participants representing a variety of community groups from Fort McMurray one-year after the fires. From the study, three factors emerged as influential to the recovery decision-making experiences of these community groups: organizational relationships, organizational capacity and the perceived value of non-profit organizations. Overall, this study suggests that these factors may influence how disaster-impacted communities solicit, identify and prioritize the competing interests of their constituent members during recovery, and therefore highlights potential areas for further research into community participation during recovery from disasters.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".