Community knowledge exchange in research leads to innovation and action in the Arctic
Bibliographic record
Abstract
Indigenous peoples from Arctic communities who are engaged in various aspects of science, research, and community work have much to share with one another. Here, we—a team of Indigenous Arctic community members and visiting researchers—present examples of such sharing during and after four knowledge exchange gatherings that formed the core of a multi-year project, bringing together Indigenous scholars, harvesters, whalers, herders, leaders, and allies from Arctic regions of Canada, Alaska, Greenland, Finland, and Siberia. We present community-to-community knowledge exchange and describe specific examples of exchange approaches, methods, successes, and challenges, as well as direct outcomes from facilitating these interactions. We suggest that the remarkable nature of those outcomes was the result not of something unique to our project, but of the inherent value of creating a venue and opportunities where people felt at ease conversing with others from elsewhere in the Arctic through loosely structured conversations that were situated in place. By not having rigid objectives to follow, participants were able to explore topics that mattered most to them. We argue this approach can lead to un-anticipated results which are best embedded in Indigenous Arctic communities. This paper demonstrates some of such results. The lessons learned through this experience are intended to contribute to conversations on research that are designed to practically respond to community needs.
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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 | 0.069 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.033 | 0.044 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".