From Resilient to Thriving: Policy Recommendations to Support Health and Well-being in the Arctic
Bibliographic record
Abstract
In 2018 – 19, eight Indigenous and non-Indigenous individuals from Canada, Greenland/Denmark, Sweden, and Alaska/United States came together to address research questions relevant to Arctic nations’ shared challenges and opportunities. Our work incorporated critical, community-based perspectives on Arctic health and well-being and promoted strengths-based approaches developed in partnership with Arctic communities. In this article we describe the group’s 16 action-oriented policy recommendations to support health and well-being in the Arctic in four thematic areas: 1) acknowledge and integrate Indigenous rights and knowledges, 2) implement meaningful action to address Indigenous determinants of health, 3) expand health-oriented monitoring and assessment programs, and 4) implement community-led, critical research approaches that focus on partnerships, reciprocity, adherence to ethical guidelines, and funding community-based research. Our recommendations are actionable guidelines for policy and research aimed at reducing inequities, supporting Indigenous expertise and existing knowledge, and promoting thriving communities in the Arctic.
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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 | 0.049 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".