<i>Aajiiqatigiingniq</i> : An Inuit Consensus Methodology in Qualitative Health Research
Bibliographic record
Abstract
Indigenous knowledge and approaches to health research have historically been marginalized by Western traditions. Efforts to overcome this marginalization by recognizing Indigenous methodologies as a distinctive form of inquiry are gathering momentum. Health research that seeks to establish levels of agreement about disputed or conceptually unclear subjects frequently relies on consensus methods. Aajiiqatigiingniq is a principle of cultural knowledge and a consensus decision-making approach among Inuit in the Canadian Arctic. We used group meetings and individual interviews involving Inuit elders and other senior community members in Arviat, Nunavut, to explore and describe aajiiqatigiingniq as an appropriate and ethical methodology in qualitative health research. Findings reveal a systematic but apparently informal approach focused on sustained individual and community well-being. Consensus is achieved through the successive addition of group members, respectful communication, mainly narrative discourse, subjective personal engagement, and an unhurried meeting style. While previous research has used Western consensus methods to embed Inuit knowledge in health research, this study provides a first descriptive account of a wholly Inuit consensus methodology.
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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 | 0.087 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".