Adapting wildland fire governance to climate change in Alaska
Bibliographic record
Abstract
We use concepts drawn from the adaptive governance literature to examine challenges and opportunities for fire management in Alaska, where rising average summer temperatures over the past several decades are associated with statewide increases in wildland fire activity.Alaska's unique interagency fire management structure, rapidly changing climate, and natural resource dependent communities provide a valuable context for study.Our research sought to understand (1) current and future fire management challenges and responses to those challenges; (2) governance structures and processes that act as enablers of and barriers to changes in management approaches; and (3) the institutionalization of new practices.We explored these questions in a qualitative analysis of 41 interviews with fire managers.Participants perceived protection of communities, enhancement of subsistence hunting opportunities, and protection of remote points on the landscape as the most pressing current management challenges, with protection of ecosystem carbon sinks as a possible future challenge.Interviewees identified existing bridging organizations and boundary-spanning work as enabling factors in the governance system.At the same time, they indicated that federal agency budgeting processes, prescriptive laws that mandate the protection of certain values, and divisions across fire and land management personnel and planning processes can inhibit effective responses to management challenges.We found evidence of several types of institutional changes, some underway, and some perceived as necessary in the future.Our research suggests that in a thick institutional context, existing institutions that serve to bridge across actors likely can be repurposed to meet new challenges, while more prescriptive institutions may be less adaptive to changing conditions.This work provides an empirical investigation of adaptive governance in a rapidly changing system and contributes to theory building on institutionalization by shedding light on the nuances and complexities of institutional work.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".