Toward Alaska Native research and data sovereignty: Observations and experiences from the Yukon Flats
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
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.
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 | 0.004 | 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) | 0.000 | 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 it