Patterns of preference and practice: bridging actors in wildfire response networks in the American Northwest
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The roles of bridging actors in emergency response networks can be important to disaster response outcomes. This paper is based on an evaluation of wildfire preparedness and response networks in 21 large-scale wildfire events in the wildland-urban interface near national forests in the American Northwest. The study investigated how key individuals in responder networks anticipated seeking out specific people in perceived bridging roles prior to the occurrence of wildfires, and then captured who in fact assumed these roles during actual large-scale events. It examines two plausible, but contradictory, bodies of theory-similarity and dissimilarity-that suggest who people might seek out as bridgers and who they would really go to during a disaster. Roughly one-half of all pre-fire nominations were consistent with similarity. Yet, while similarity is a reliable indicator of how people expect to organise, it does not hold up for how they organise during the real incident.
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.
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it