Choose your collaborators wisely: Addressing interdependent tasks through collaboration in responding to wildfire disasters
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
Abstract Responding to disastrous wildfires traversing geographical scales requires multi‐actor collaboration to address a series of interdependent operational tasks. While this type of distributed collective action problem is salient across governance contexts, less is known about if and how collaboration helps individual actors effectively address their tasks. Applying a novel network‐centric method to wildfire responder networks in Canada and Sweden, this study shows that when actors working on the same tasks collaborate, and/or when one actor addresses two interdependent tasks, effectiveness increases. The number of collaborative ties an actor has with others does not enhance effectiveness. Furthermore, when the chain of command is unclear, and/or when actors lack recent disaster management experience and/or pre‐existing collaborative relationships, effectiveness only increases if multiple actors collaborate over multiple interdependent tasks. The results have implications for disaster response agencies, and they provide valuable insights for collaborative responses to significant societal and environmental challenges.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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