Comparative review of One Health and Indigenous approaches to wildlife research in Inuit Nunangat
Bibliographic record
Abstract
There is increasing interest in One Health and Indigenous methodologies and approaches in wildlife research, but they are not widely used research applications in the Arctic. Both approaches are wide in scope and originate from different knowledge systems but are often compared synonymously. We review the literature of overlap between the term One Health and Inuit Qaujimajatuqangit (Inuit Indigenous Knowledge) throughout Inuit Nunaat on wildlife research. Three databases (SCOPUS, Web of Science, and BIOSIS) were used to find English language articles and books within the bounds of Inuit Nunaat. While One Health and Inuit Qaujimajatuqangit research approaches share synergies, they are fundamentally disparate owing to their differences in epistemology, including views on the natural environment and wildlife management. We describe current examples of One Health being operationalized in Inuit Nunaat and identify potential to address larger and more complex questions about wildlife health, with examples from terrestrial and marine Arctic wildlife. Both Indigenous methodologies and One Health naturally have a human component at their core, which seamlessly lends itself to discussions on wildlife management, as human actions and regulations directly impact environment and wildlife health.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".