Working with an Indigenous Advisory Council to facilitate effective communication and collaboration between researchers and Arctic communities 
Bibliographic record
Abstract
The Arctic Rivers Project is a National Science Foundation – Navigating the New Arctic funded project aimed at increasing our understanding of the impacts of climate change on rivers, fish, and Indigenous communities across the Northern Alaska and the Yukon River Watershed in Alaska and Canada. This will be accomplished through water-quality monitoring, a variety of modeling activities, and the development of narratives of change from community members themselves. Combined these methods will create storylines of climate change in the arctic. Storylines combine experiential narrative information with model outputs to make the predicated future more tangible regarding potential impacts. The project team is comprised of researchers from the natural and social sciences as well as the modeling community and two Indigenous organizations focused on science, outreach, and engagement. To increase the research team’s ability to co-produce knowledge with Indigenous communities across a large study domain we are working with an Indigenous Advisory Council (IAC). The IAC is comprised of 11 Indigenous community members, leaders, elders and students representing diverse communities across our study domain. The IAC meets via online video conferencing monthly to tackle tasks such as developing knowledge co-production and inclusion and protection of Indigenous Knowledge protocols to guide the project. Additionally, the IAC is working with a subset of the research team to create the goals, objectives, and agenda for an Arctic Rivers Summit that will bring together Tribal and First Nation resource managers, Arctic and Boreal community members, and academic, Indigenous, federal, state, and provincial researchers to unify the state of knowledge on Arctic Rivers as a community of observers, investigators, knowledge holders, and stewards. This presentation will discuss the steps taken to form the IAC, the role of the IAC in guiding project implementation, providing advice, and facilitating connections with Indigenous communities. It is our hope that we may provide an example of successful implementation and design to communicate and co-produce knowledge with communities across a large study domain from which other projects may learn.
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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 | 0.044 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.061 | 0.018 |
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".