Working With Indigenous Elders in Narrative Inquiry: Reflections and Key Considerations
Bibliographic record
Abstract
The Canadian research context shifted with the adoption of the report of the Truth and Reconciliation Commission and its focus on considerations of Indigenous peoples. Drawing on multiple years of working together, this article explores the experiences of members of a research team that includes Indigenous Elders. The authors revisit three significant research encounters: engaging in teachings of “silent walking,” engaging in teachings through the processes of drum making with youth, and engaging in teachings on the importance of language. Three important considerations for working in research teams with Elders are the importance of continuing to find ways to be in relation, to live reciprocity beyond the rhetoric often associated with Indigenous research, and to see our work as marked by mutuality.
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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 | 0.072 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.059 | 0.065 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.008 | 0.017 |
| 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; 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".