Policy strategies to address sustainability of Alaskan boreal forests in response to a directionally changing climate
Bibliographic record
Abstract
Human activities are altering many factors that determine the fundamental properties of ecological and social systems. Is sustainability a realistic goal in a world in which many key process controls are directionally changing? To address this issue, we integrate several disparate sources of theory to address sustainability in directionally changing social-ecological systems, apply this framework to climate-warming impacts in Interior Alaska, and describe a suite of policy strategies that emerge from these analyses. Climate warming in Interior Alaska has profoundly affected factors that influence landscape processes (climate regulation and disturbance spread) and natural hazards, but has only indirectly influenced ecosystem goods such as food, water, and wood that receive most management attention. Warming has reduced cultural services provided by ecosystems, leading to some of the few institutional responses that directly address the causes of climate warming, e.g., indigenous initiatives to the Arctic Council. Four broad policy strategies emerge: (i) enhancing human adaptability through learning and innovation in the context of changes occurring at multiple scales; (ii) increasing resilience by strengthening negative (stabilizing) feedbacks that buffer the system from change and increasing options for adaptation through biological, cultural, and economic diversity; (iii) reducing vulnerability by strengthening institutions that link the high-latitude impacts of climate warming to their low-latitude causes; and (iv) facilitating transformation to new, potentially more beneficial states by taking advantage of opportunities created by crisis. Each strategy provides societal benefits, and we suggest that all of them be pursued simultaneously.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| 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".