Equity in resilience: a case study of community resilience to wildfire in southwestern Oregon, United States
Bibliographic record
Abstract
In the fire-prone and fire-adapted landscape of the Rogue River Basin of southwestern Oregon, communities mobilize to prepare, respond, and recover from wildfire while modifying the current social and ecological system. Marginalized communities are often most affected and least prepared for disturbances of this kind, where racism, colonialism, and structural equities prevent meaningful inclusion and equitable allocation of resources. This research centers these voices in an empirical study of the situated resilience of the Rogue River Basin, rooted in the work of community-based organizations, land managers, conservation organizations, and private contractors. We take an embedded and qualitative approach, considering resilience “of what to what,” “for whom,” “by whom,” and “how” within the confines of the Rogue River Basin. We engaged those most affected by wildfire in the process of designing research, detailing experiences, and shaping outcomes. Relying on descriptive accounts and perceptions of what constitutes community resilience to wildfire, this research shows resilience is context-dependent with different paths to resilience for different groups. We co-produced multiple attributes of resilience, and describe how cross-cutting themes within attributes indicate perceived shifts from less-resilient to more resilient system states. For those in the Rogue Basin, more resilient systems involve local engagement in decision making, acknowledgment of the value of non-dominant knowledge systems, and reciprocity and shared resources between the community’s most vulnerable. We found that as actors sought more radical change through the creation of new systems, their capacity to address social inequities grew. Moreover, outcomes of this research challenge decision makers invested in community resilience to consider who benefits and is burdened not just by disturbance itself, but policies and programs designed for preparation, response, and recovery. Ultimately, in relying on lived experience, we construct policy and management recommendations in service of the communities most affected.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".