Bibliographic record
Abstract
Conflicts over natural resources are often misunderstood as being driven primarily by economic concerns or failings of human nature. However, human dimensions research has shown that conflicts are more often driven by problems and shortcomings in institutions for governance and management. In this article, we explore long-standing conflicts over the salmon fisheries of the Kenai River and Upper Cook Inlet region of Southcentral Alaska, fisheries that are embroiled in a long-standing conflict and controversy. We engaged in ethnographic research with participants from commercial, sport, and personal use fisheries in the region to understand their perceptions of these local “salmon wars.” We find that these disputes are more nuanced than is captured by existing typologies of natural resource conflicts, and argue that conflicts can take on a life of their own wherein people stop responding to each other and start responding to the conflict itself, or at least the conflict as they understand it. This perspective is helpful for understanding how conflict in the region has escalated to a point of apparent dysfunction via a process known as schismogenesis. We conclude with a discussion that considers this conflict as an indicator of institutional failure from a social justice perspective, and argue that attempts for conflict management and/or resolution in cases such as these must focus first on protecting the human rights of all participants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.013 |
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".