Aulukluki neqkat: centering care of salmon and relational research in Indigenous fisheries in the Kuskokwim River, Alaska
Bibliographic record
Abstract
This Indigenous-led project aims to better understand historical and contemporary ways in which Alaska Native Peoples steward salmon and the values connected to salmon stewardship. Indigenous Peoples have stewarded Alaska lands and waters for thousands of years yet have been largely excluded from western science and management systems. In this project, we utilize a participatory approach that allows for the equitable valuing of ideas and knowledges to document the breadth and depth of Yup'ik and Athabascan knowledge and governance systems in southwestern Alaska. We reshape research methodologies by centering Indigenous frameworks and methodologies, including circle dialogues and multi-generational interviews led by Indigenous scholars and students in their home communities and regions. In this paper, we share the Yup'ik and Athabascan values, knowledge, management, and governance mechanisms that can improve the long-term sustainability and equity of Alaska salmon systems. This research elevates the voices of Alaska Native salmon stewards and experts from the Kuskokwim Bay and the Kuskokwim River. We elaborate on five key themes that emerged from this research, including traditional Indigenous ways of life, Indigenous stewardship, self-determination, food and livelihood sovereignty, and ecosystem changes, and identify a more equitable and sustainable path forward for salmon and people in Alaska.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".