Maintaining the Integrity of Indigenous Knowledge; Sharing Metis Knowing Through Mixed Methods
Bibliographic record
Abstract
Working collaboratively with Indigenous populations necessitates a focus on partnerships at the core of sharing, implementing and disseminating Indigenous knowledge. The Tri-Council Policy 2 ISSN: ISSN 1837-0144 © International Journal of Critical Indigenous Studies Statement (CIHR, 2010) notes that respectful, reciprocal and ethical research standards must be applied to research with Indigenous communities. Métis collaborators identified that relationships must be regarded as the central focus of sharing Metis knowledge. Utilizing an investigation on the health benefits of participating in cultural activities, specifically harvesting, we demonstrate how applying mixed methods meets and informs these research standards and creates a unique, participatory Indigenous research method relevant for Métis people. Building from these research standards, this collaboration developed a method of investigation that shares Indigenous knowledge of population health. This method promotes a sustainable research relationship, moving beyond fragmented research projects and making relational connections between people, data sources and findings
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".