Factors Influencing Adaptive Capacity in the Reorganization of Forest Management in Alaska
Bibliographic record
Abstract
Several studies of U.S. National Forests suggest that declines of their associated forest products industries were driven by synergistic changes in federal governance and market conditions during the late 20th century. In Alaska, dramatic shifts in the economic and political settings of the Tongass National Forest (Tongass) drove changes in governance leading to collapse of an industrial forest management system in the early 1990s. However, 15 years since collapse, the reorganization of Tongass governance to reflect 'new' economic and political realities has not progressed. To understand both the factors that hinder institutional change (inertia) and the factors that enable progress toward reorganization (adaptation), I analyzed how Tongass forest management, specifically timber sale planning, has responded to changes in market conditions, local industry structure, and larger-scale political governance. Inertia was evidenced by continued emphasis on even-aged management and large-scale harvesting, i.e., the retention of an industrial forestry philosophy that, in the current political situation, yields mostly litigation and appeals, and relatively few forest products. Adaptation was evidenced by flexibility in harvest methods, a willingness to meet local demand instead of political targets, and a growing degree of cooperation with environmental advocacy groups. New partnerships, markets, and political leaders at state and national levels can frame a new blueprint for reorganization of Tongass management toward a more sustainable future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".