Like a Fish Out of Water: Reconsidering Disaster Recovery and the Role of Place and Social Capital in Community Disaster Resilience
Bibliographic record
Abstract
In this paper we draw on the findings of a critical, multi-sited ethnographic study of two rural communities affected by a wildfire in British Columbia, Canada to examine the salience of place, identity, and social capital to the disaster recovery process and community disaster resilience. We argue that a reconfiguration of disaster recovery is required that more meaningfully considers the role of place in the disaster recovery process and opens up the space for a more reflective and intentional consideration of the disorientation and disruption associated with disasters and our organized response to that disorientation. We describe a social-psychological process, reorientation, in which affected individuals and communities navigate the psychological, social and emotional responses to the symbolic and material changes to social and geographic place that result from the fire's destruction. The reorientation process emphasizes the critical importance of place not only as an orienting framework in recovery but also as the ground upon which social capital and community disaster resilience are built. This approach to understanding and responding to the disorientation of disasters has implications for community psychologists and other service providers engaged in supporting disaster survivors. This includes the need to consider the complex dynamic of contextual and cultural factors that influence the disaster recovery process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".