Building Resilience to Climate Change in Rural Alaska: Understanding Impacts, Adaptation and the role of Tek
Bibliographic record
Abstract
The arctic system is undergoing significant change, warming at twice the rate of the rest of the world due to anthropogenic climate change. Through interviews with Alaska Native communities along the Yukon River, this study documents traditional knowledge relating to the changes that are occurring in the weather and on the landscape, effects on subsistence livelihoods, and adaptation strategies. People observed significant changes, many of which are consistent with conventional scientific studies on ecosystem change in Alaska. They responded to environmental change by spreading risk across resources, space, time and households. Since many current and historical adaptation practices are embedded in the subsistence lifestyle and fall under federal and state management, this study also explored the effects of management on resilience. Respondents viewed subsistence management regimes as detrimental to livelihoods when they did not prioritize subsistence uses over recreational and commercial uses of natural resources, provide for local involvement in regulatory and management decisions, and when they constrained subsistence harvesters’ ability to pursue resources when they were available or most needed. People saw a benefit to agency management of subsistence when management actions supported local priorities, such as predator control. People viewed subsistence management even more favorably when local communities were able to voice concerns and have a role in shaping decisions. Community resilience to climate change is in part determined by the ability to accumulate knowledge and act collectively, and is enhanced by the ability to participate in the decisions that affect the flow of resources. Therefore, management actions that inhibit the exchange of information and collective action, qualities inherent to many traditional knowledge systems, may undermine the resilience of Alaska Native communities to climate change. However, integrating traditional knowledge with natural resource management and allowing communities to participate in decisions through collaborative and co-management arrangements, if done in a way that allows traditional knowledge to actually shape management outcomes, may enhance resilience.
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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 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".