Lessons Learned through Research Partnership and Capacity Enhancement in Inuit Nunangat
Bibliographic record
Abstract
Facilitating research and enhancing community research capacity through a partnered approach in Inuit Nunangat (the Inuit homeland of Canada, located in Arctic Canada) presents learning opportunities and challenges for southern-based, non-Inuit researchers and community members alike. This article outlines lessons learned through the Arctic Corridors and Northern Voices (AC-NV) project, which involved 14 communities across Inuit Nunangat. The AC-NV focused on understanding community-identified impacts and potential management options of increased shipping in Inuit Nunangat due to sea ice reductions and a changing climate. The approach used to conduct the research involved visiting researchers and community partners working together with local organizations, and training and hiring northern youth as cultural liaisons and workshop co-facilitators. We strove to develop a model of collaborative partnership and strong north-south research relationships. In this paper, we draw on our broad learning experiences from four community case studies conducted as part of the AC-NV project: Arviat, Cambridge Bay, Gjoa Haven, and Pond Inlet, Nunavut. Close partnerships were formed in each of these communities, and 32 youth were trained in participatory mapping and workshop facilitation. For our diverse team of Inuit, northern- (i.e., non-Inuit, living in Inuit Nunangat), and southern-based non-Inuit researchers, our efforts to engage in partnered research were a critical component of the research and learning experience. In this article we share methodological reflections and lessons learned from what collaborative-partnered research means in practice. In so doing, we aim to contribute to the increasing dialogue and efforts around knowledge co-production and Inuit self-determination in research. Key conclusions of this reflective exercise include the importance of 1) conducting research that is relevant to local needs and interests, 2) visiting researchers and local organizations partnering together, 3) co-creating and refining knowledge documentation tools, 4) including youth cultural liaisons as co-facilitators, 5) conducting results validation and sharing exercises, and 6) being open to forming personal friendships. For the AC-NV, this community-based partnership approach resulted in more robust research results, strengthened north-south relations, and enhanced local capacity for community-led projects.
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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 | 0.030 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.020 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".