Where are the people? A scoping review on the use of the term “resilience” in Arctic health research and its relevance to community expressions of well-being
Bibliographic record
Abstract
In the field of Arctic health, “resilience” is a concept used to describe the capacity to recover from adversities. The term is widely used in Arctic policy contexts; however, Arctic peoples and communities question whether “resilience” is an appropriate term to describe the human dimensions of health and well-being in the Arctic as it is currently applied. A scoping review of peer-reviewed and gray literature was conducted. We used searchable databases, Google Scholar, and Dartmouth College Library Services, to select studies conducted between 2000 and 2019 and key documents from the Arctic Council and other relevant organization and government entities. A scoping review framework was followed, and consultation among the authors provided initial scope, direction, and verification of findings. Analyses identified over- and underrepresented key thematic areas in the literature on human resilience in Arctic communities. Areas of overrepresentation in the literature included ecosystem, climate change, and environmental sciences. Areas that were underrepresented in the literature included health, medicine, wellness or well-being, and community voices on the topic of human resilience. Results indicated that “resilience” as a concept was applied across a diversity of contexts and subject areas in the Arctic and that this may have repercussions for understanding the human dimension of “resilience” and community expressions of well-being. Alternative terms and concepts with which Northern community members more closely identify could be used to more respectfully and accurately advance research in areas such as epidemiology, community health and well-being, and particularly Indigenous peoples’ health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".