Applying the Aajiiqatigiingniq Research Methodology: approaches and lessons learned through collaborative survey development and analysis
Bibliographic record
Abstract
Aajiiqatigiingniq is an Inuit Qaujimajatuqangit principle used to discuss serious topics and come to consensus around decision-making. In working with Elders on community research issues, the Aqqiumavvik Society in Arviat, Nunavut developed the Aajiiqatigiingniq Research Methodology (ARM) as a participatory and fully inclusive Inuit research approach. We followed the ARM in establishing partnerships, and developing and facilitating a survey, to learn how Nunavut community members use and share available weather, water, ice, and climate information. The ARM guided our 5-year research process involving 19 Local Research Coordinators, along with 13 Nunavut-based, eight university, and three federal government collaborators. Through community research leadership and connecting long-term partnerships, we worked in eight Nunavut communities. In this paper, we present the opportunities and challenges of putting the ARM into practice through four research stages: (1) building relationships; (2) building shared understanding; (3) knowledge sharing; and (4) collaborative analysis. In this process, we have come to recognize our strength in connection, Local Research Coordinators as Highly Qualified Personnel, the four Rs of collaborative analysis, and the challenges of hurdling institutions. We share our approach and lessons learned to contribute to ongoing efforts and dialogue around decolonizing research and enhancing Inuit self-determination in 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 | 0.253 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".