Ways to Help and Ways to Hinder: Governance for Effective Adaptation to an Uncertain Climate
Bibliographic record
Abstract
This paper compares two case studies in Alaska, one on commercial fishers of the Bering Sea and Aleutian Islands region and the other on moose hunters of Interior Alaska, to identify how governance arrangements and management strategies enhance or limit people’s ability to respond effectively to changing climatic and environmental conditions. The two groups face similar challenges regarding the impacts of a changing climate on wild fish and game, but they tell very different stories regarding how and under what conditions these impacts challenge their harvest activities. In both regions, people describe dramatic changes in weather, land, and seascape conditions, and distributions of fish and game. A key finding is that the “command-and-control” model of governance in the Alaska Interior, as implemented through state and federal management tools such as registration hunts and short open seasons, limits effective local responses to environmental conditions, while the more decentralized model of governance created by the Limited Access Privilege systems of the Bering Sea allows fishers great flexibility to respond. We discuss ways to implement aspects of a decentralized decision-making model in the Interior that would benefit hunters by increasing their adaptability and success, while also improving conservation outcomes. Our findings also demonstrate the usefulness of the diagnostic framework employed here for facilitating comparative crossregional analyses of natural resource use and management.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".