Building community resilience to wildfire risks in the Robson Valley, British Columbia, Canada
Bibliographic record
Abstract
This thesis examines how rural communities are at risk to wildfire hazards through a case study of the Robson Valley, British Columbia, Canada. The research is guided by a vulnerability approach, which conceptualizes risk as a function of how a community is exposed and sensitive to a hazard and its capacity to adapt. Data were collected using semistructured interviews with policymakers, forest professionals and emergency managers alongside community meetings in three rural areas, participant observation, and analysis of secondary sources. The findings show that while most communities in the Robson Valley are not directly at risk from extreme wildfire hazards, they are indirectly exposed and sensitive to secondary and tertiary impacts, due to a single power transmission and road transportation route, that are both highly exposed to wildfire hazards. The centralization of government services has led to a change in the ways that wildfires are suppressed, which can be incongruent with diverse land values and attitudes about responding to hazards held by longtime residents and local First Nations. This thesis concludes with recommendations for how to better engage rural communities in fire prevention and suppression including the creation of a community champion position and improved legislation allowing for the participation of rural residents in fire suppression operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".