Expertise and Inequality Amid Environmental Crisis: A View from the Yukon-Kuskokwim Delta
Bibliographic record
Abstract
ABSTRACT Scientific expertise is crucial for responding effectively to environmental crises. Nevertheless, under conditions of political inequality, expert policy making can inhibit policy solutions by altering incentives of powerful interest groups. This is the situation facing the predominantly Alaska Native communities of the Yukon-Kuskokwim Delta, which have long relied on salmon for subsistence and are now experiencing a collapse of the salmon population. Scientific evidence indicates that climate change is a primary cause, and experts therefore have opposed demands by Native subsistence fishers for ameliorative measures—especially restricting pollock fishing—as likely to be ineffective. However, this approach eliminates incentives for the influential pollock industry to support policies to address the salmon crisis, including climate-change mitigation. This article presents a simple formal model that demonstrates these incentive effects. This argument contributes to theories of business power and shows how expert policy making can inadvertently force marginalized communities to bear the burden of climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".