Ethnographic Data from Riverbank Erosion Study with Alaska Yukon River Watershed Communities 2022-2026
Bibliographic record
Abstract
The overarching goal of the NNA (Navigating the New Arctic) collaborative project: Developing capacity for planning and adapting to riverbank erosion and its consequences in the Yukon River Basin was to develop capacity for planning and adapting to riverbank erosion and its consequences in the Yukon River basin. Despite the enormous challenge posed by bank erosion and its societal impact, we conducted the study to understand how erosion will respond to warming, how the ensuing biogeochemical processes will affect water quality, and how communities will adapt to these changes. In order to develop effective risk mitigation strategies for adaptation planning, local communities and regional institutions need to know the spectrum and probability of risks associated with changing environmental processes. This dataset from the social science component of the project includes: ethnographic data collected during community visits to partner communities in the Yukon River Watershed: participant observation fieldnotes, interview notes and transcripts, tribal council erosion action group meetings notes and transcripts, images, and community plans.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".