Diverse methodological approaches to a Circumpolar multi-site case study which upholds and responds to local and Indigenous community research processes in the Arctic
Bibliographic record
Abstract
This paper outlines the methodological approaches to a multi-site Circumpolar case study exploring the impacts of COVID-19 on Indigenous and remote communities in 7 of 8 Arctic countries. Researchers involved with the project implemented a three-phase multi-site case study to assess the positive and negative societal outcomes associated with the COVID-19 pandemic in Arctic communities from 2020 to 2023. The goal of the multi-site case study was to identify community-driven models and evidence-based promising practices and recommendations that can help inform cohesive and coordinated public health responses and protocols related to future public health emergencies in the Arctic. Research sites included a minimum of 1 one community each from Canada (Nunavut,) United States of America (Alaska), Greenland, Iceland, Norway, Sweden, Finland. The approaches used for our multi-site case study provide a comprehensive, evidence-based account of the complex health challenges facing Arctic communities, offering insights into the effectiveness of interventions, while also privileging Indigenous local knowledge and voices. The mixed method multi-site case study approach enriched the understanding of unique regional health disparities and strengths during the pandemic. These methodological approaches serve as a valuable resource for policymakers, researchers, and healthcare professionals, informing future strategies and interventions.
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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 | 0.127 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".