Conflict and Collaboration in Wildfire Management: The Role of Mission Alignment
Bibliographic record
Abstract
Abstract Responding to large wildfires requires actors from multiple jurisdictions and multiple levels of government to work collaboratively. The missions and objectives of federal agencies often differ from those of state land management agencies as well as local wildfire response agencies regarding land use and wildfire management. As wildfire size and intensity increase over time and associated annual suppression costs range between $2 billion and $3 billion, learning more about the existence and management of perceived agency differences becomes imperative within the academic and practitioner communities. This article examines the extent to which perceived mission misalignment exists among federal, state, and local actors and how well those differences are managed. Findings provide quantitative evidence that mission misalignment is greater within intergovernmental relationships than within intragovernmental relationships. Additionally, findings speak to the larger conversation around intergovernmental relationships within the federal structure and perceptions of the presence and management of potential interagency conflict . Practitioner Points Potential conflict between the missions of federal and state land agencies presents a challenge for disaster management, and differing governmental levels and land‐use mandates may highlight relationships where tensions are likely greater. Wildfire managers may need to more proactively address relationships among federal agencies and state and local partners rather than relationships among multiple federal agencies. Wildfire management may benefit from increased awareness of—and discussion around—partner agencies’ stated land management philosophies and legal mandates, as structural frameworks, such as the Incident Command Structure, may not alone lead to conflict‐free collaboration.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".