Echoes, transgressions, and transformations : identity reorientation and the discourse of disaster recovery
Bibliographic record
Abstract
Little attention has been paid to the discursive framework that guides the experiences of "recovery" from natural disasters and associated health-related consequences. Traditional psychological paradigms in disaster studies have tended to adopt a mechanistic view that controls for complexity by minimizing the sociocultural and gendered contexts of disaster recovery and accepting uncritically the ideological assumptions that guide the process. This has resulted in a narrow framing of recovery that focuses primarily on the economic and material consequences of disasters and promotes a speedy return to the status quo. This study begins to address this gap in research and practice by adopting a complex-systems and critical perspective to examine the psychosocial process and discursive practices of disaster recovery. A critical, multi-sited ethnographic approach was applied to study the recovery process in two rural communities in British Columbia, Canada, where the McLure Fire forest fire destroyed homes and businesses, ravaged the landscape, and devastated the economic sustainability of the region. Qualitative analysis methods, including strategies from social constructivist grounded theory, methods from critical discourse analysis, and creative writing strategies were employed to examine interviews with residents, local news media texts, and the reflections of the researcher for themes and guiding and informing discourses. A social-psychological process described as disorientation and reorientation was identified in which residents navigated and negotiated shifts in their material and social frames of reference associated with the material and symbolic losses incurred as a result of the McLure Fire. Reorientation involved a complex process of recreating and redefining individual and collective identities. Despite the complexity of this process, the dominant discursive practice of disaster recovery identified in both the media and interview accounts continued to emphasize the economic and material aspects of recovery. This had the effect of marginalizing and sequestering suffering, and constrained opportunities for individual and social capacity building. The findings suggest that adopting a complex systems approach to disaster might result in new more flexible and empowering practices. Including a mindfulness approach to the disorientation of disasters would focus attention on emergent possibilities and the creative potential of the identity reorientation process during recovery from a disaster.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".