Exploring the Term “Resilience” in Arctic Health and Well-Being Using a Sharing Circle as a Community-Centered Approach: Insights from a Conference Workshop
Bibliographic record
Abstract
In the field of Arctic health, “resilience” is a term and concept used to describe capacity to recover from difficulties. While the term is widely used in Arctic policy contexts, there is debate at the community level on whether “resilience” is an appropriate term to describe the human dimensions of health and wellness in the Arctic. Further, research methods used to investigate resilience have largely been limited to Western science research methodologies, which emphasize empirical quantitative studies and may not mirror the perspective of the Arctic communities under study. To explore conceptions of resilience in Arctic communities, a Sharing Circle was facilitated at the International Congress on Circumpolar Health in 2018. With participants engaging from seven of the eight Arctic countries, participants shared critiques of the term “resilience,” and their perspectives on key components of thriving communities. Upon reflection, this use of a Sharing Circle suggests that it may be a useful tool for deeper investigations into health-related issues affecting Arctic Peoples. The Sharing Circle may serve as a meaningful methodology for engaging communities using resonant research strategies to decolonize concepts of resilience and highlight new dimensions for promoting thriving communities in Arctic populations.
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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 | 0.036 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.041 | 0.029 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".