Bibliographic record
Abstract
• Narratives about the wildland urban interface homogenize experiences with wildfire. • We need to expand our idea of geographies of risk associated with wildfire. • People who live in fire adapted landscapes are potential stewards. Place-based stewardship in mountain watersheds can play an important role in responding to and helping to mitigate different kinds of landscape-based hazards, including wildfire and post-fire flooding. In a world with more extreme wildfire, what does it mean to live with fire, where the impacts affect people and places across jurisdictions for many years after the fire itself? Drawing on over two years of ethnographic fieldwork including participant observation and interviews, I explore this question in the particular geographic and land use context of the Poudre Canyon in northern Colorado. Local stewardship practices in mountain communities can benefit populations beyond the fire perimeter. These may be rooted in ongoing relationships to the land, local, volunteer-based hazard mitigation and emergency response, and post-fire collaborative efforts. Using narrative analysis, I examine how landscapes at risk of fire are defined, how wildfire risk is communicated to those living in these landscapes in Colorado, and how discourses of risk and responsibility facilitate or constrain adaptation to living with extreme fire. I encourage an approach to risk communication that conceptualizes those who live in fire adapted landscapes as potential stewards. I also suggest that wildfire risk reduction efforts should be more geographically and socially expansive, to acknowledge that contending with wildfire and its associated hazards of smoke and flooding is a society-wide challenge, not just for those living in fire adapted landscapes. Living with fire is a process, and community members, practitioners, and scientists alike are reorienting toward a world with more extreme wildfire.
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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.023 | 0.052 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".