Methods and approach to the interdisciplinary and cross-cultural <i>Arctic Alaska Salmon Workshop</i>: critical self-reflections from fisheries scientists
Bibliographic record
Abstract
Redistribution and shifting habitat envelopes are impacting organisms across many taxa, which in turn are impacting Indigenous ways of life. In Arctic Alaska, Pacific salmon are known to have occurred for at least a century, but in recent years appear to be increasingly common. With the goal of holistically understanding and describing these changes in a way that equitably considers Indigenous, local, and western knowledge, we share our experience and methodologies in facilitating the Arctic Alaska Salmon Workshop. We share our perspective, approach, and methods as fisheries natural scientists convening this workshop, which included community-based knowledge holders from the Iñupiat communities of Kotzebue, Point Hope, Utqiaġvik, and Kaktovik, and western scientists and researchers from universities, fishery management agencies, and local community government. After briefly discussing some of the workshop highlights, we conclude with four key takeaways: (1) that the process of co-production of knowledge is an ideal towards, which we must strive, but acknowledge we may rarely, if ever, fully achieve, (2) pursuit of the “good science” should guide our work, (3) examination and assessment of assumptions should occur early and often, and (4) Anglanikina! Make sure you have a good time, (Yup'ik).
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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 | 0.088 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".