Niqivut Silalu Asijjipalliajuq: Building a Community-Led Food Sovereignty and Climate Change Research Program in Nunavut, Canada
Bibliographic record
Abstract
The history of health research in Inuit communities in Canada recounts unethical and colonizing research practices. Recent decades have witnessed profound changes that have advanced ethical and community-driven research, yet much work remains. Inuit have called for research reform in Inuit Nunangat, most recently creating the National Inuit Strategy on Research (NISR) as a framework to support this work. The present study details the process undertaken to create a research program guided by the NISR to address food security, nutrition, and climate change in Inuit Nunangat. Four main elements were identified as critical to supporting the development of a meaningful and authentic community-led program of research: developing Inuit-identified research questions that are relevant and important to Inuit communities; identifying Inuit expertise to answer these questions; re-envisioning and innovating research methodologies that are meaningful to Inuit and reflect Inuit knowledge and societal values; and identifying approaches to mobilizing knowledge that can be applied to support food security and climate change adaptation. We also identify considerations for funding agencies to support the meaningful development of Inuit-led research proposals, including aligning funding with community priorities, reconsidering who the researchers are, and investing in community infrastructure. Our critical reflection on the research program development process provides insight into community-led research that can support Inuit self-determination in research, enhance local ethical conduct of research, privilege Inuit knowledge systems, and align Inuit-identified research priorities with research funding opportunities in health research. While we focus on Inuit-led research in Nunavut, Canada, these insights may be of interest more broadly to Indigenous health research.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.041 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".