Nunaaqqit Savaqatigivlugich—working with communities: evolving collaborations around an Alaska Arctic observatory and knowledge hub
Bibliographic record
Abstract
Indigenous Peoples across the Arctic have adapted to environmental change since time immemorial, yet recent climate change has imposed unprecedented and abrupt changes that affect the land and sea upon which communities rely. Co-created community-based observing programs offer an opportunity to harness the holistic breadth of knowledge in communities with the goal of tracking Arctic change while simultaneously supporting community priorities and local-scale needs. The Alaska Arctic Observatory and Knowledge Hub (AAOKH) is a network of Iñupiaq observers from northern Alaska coastal communities working in partnership with academic researchers. Here, we describe five core functions that have emerged through AAOKH, which include tracking long-term environmental changes; communicating Indigenous-led observations of the environment and their meaning; place-based and culturally relevant education; enabling scientific and Indigenous Knowledge exchange; and supporting community-led responses to environmental change. We outline and discuss specific actions and opportunities that have been used to increase knowledge exchange of AAOKH observations, make space for the next generation of Indigenous scholars, and create locally relevant data products and syntheses that can inform resource management and community planning. We also discuss our ongoing efforts to increasingly shift toward a knowledge coproduction framework as we plan to sustain AAOKH into the future.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".