One Health Gathering: Arctic Indigenous Peoples Voices and Perspectives
Bibliographic record
Abstract
The proceedings of the One Health Gathering in Iqaluit, Nunavut, Canada, centered on the theme of "Elevating Indigenous Voices in One Health Research in the Arctic." Approximately fifty participants from Greenland, the USA, Canada, and across Nunavut's three regions convened to explore key aspects of research pertinent to this theme. The gathering underscored Indigenous Knowledge and Practices, emphasizing innovative post-secondary education models rooted in Inuit ways of knowing. It also delved into Holistic Worldviews and Health and Wellbeing, spotlighting community country food programs, harvesting practices, and the significance of holistic perspectives in healthcare. Local contexts and community voices were prioritized, with presentations showcasing community-led testing for a parasite in walrus, co-management programs for polar bears, and the vital role of student voices and perspectives. Elder stories and wisdom were shared, adding invaluable insight and depth to discussions. Moreover, the gathering fostered community empowerment and action on One Health research and/or policy, culminating in collaborative recommendations and an art piece aimed at amplifying community engagement and agency in research initiatives. Overall, the event provided a platform for diverse voices to converge, exchange knowledge, and collaborate towards a more inclusive and effective approach to One Health research in the Arctic, reflecting a commitment to Indigenous perspectives, community empowerment, and holistic wellbeing.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.063 | 0.016 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".