Toward Alaska Native research and data sovereignty: Observations and experiences from the Yukon Flats
Bibliographic record
Abstract
Indigenous Peoples research and data sovereignty is of paramount importance to a healthy relationship between Indigenous Peoples and the research enterprise. The development of Indigenous methods and methodologies lends itself to the hot discussion of research and data or, as we posit, knowledge born from Alaska Native communities’ experiences and observations since time immemorial. Within the context of climate change, Alaska Native communities in the Yukon Flats National Wildlife Refuge (Flats) are experiencing research fatigue. There are an extraordinary number of researchers applying constant pressure on Alaska Native communities on the Yukon Flats to engage with research ideas and pursuits that are not of their own needs. In concert with large and frequent grant dollars that are promoting research with Alaska Native Peoples and demand grant proposals have components of coproduction of knowledge intertwined with the research. With so much research directed at, not with, Alaska Native communities on the Yukon Flats, never has it been more important to shape research and data sovereignty with Alaska Native communities based on their needs and their worldviews. This article works to demonstrate how established Indigenous methods in collaboration with Alaska Native and Allies scholarship alongside Alaska Native communities inform the future of Alaska Native research and data sovereignty.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".