Bibliographic record
Abstract
The Circumpolar region, comprising the Arctic territories encircling the North Pole, is home to diverse Indigenous cultures facing unique socio-economic challenges. Indigenous communities such as the Inuit, Sámi, Athabaskan, Gwitchin, and Russian Arctic groups exhibit rich traditions and adaptive practices tied to their environments. Environmental diversity, from icy tundra to boreal forests, influences livelihoods and biodiversity, while significant socio-economic disparities persist, impacting access to healthcare, education, and economic opportunities. Against this backdrop, the global COVID-19 pandemic accentuated the intersection of environment, culture, and health in remote Arctic regions, presenting distinct challenges and opportunities. Initiated by a collaborative research project led by Fulbright Arctic Initiative Alumni, this special issue of the International Journal of Circumpolar Health explores the impacts of COVID-19 on Arctic Indigenous and rural communities. Building on previous work and recommendations, the issue features community case studies, highlighting community experiences and collaborative approaches to understand and address the pandemic's effects. The authors highlight both positive and negative societal outcomes, presenting community-driven models and evidence-based practices to inform pan-Arctic collaboration and decision-making in public health emergencies. Through sharing these insights, the special issue aims to privilege local and Indigenous knowledge systems, elevates community responses to complex and multifaceted challenges, and contributes to the evidence base on global pandemic response.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".