Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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 it