Indigenous methodologies walking together in a good way: urban Indigenous collective governance in health research
Bibliographic record
Abstract
Indigenous methodology is a living methodology of doing research in a good way that honours respectful relationships with Indigenous Peoples and communities in which knowledge is co-created and ownership is shared. Guided by Indigenous methodologies, the Urban Indigenous Collective Governance Circle was co-developed for urban Indigenous health research. The Collective Governance uses approaches that stay true to the connectedness of Traditional Knowledges, Indigenous protocols, and relational processes. Relationality ensures guidance from knowledge, experiences, and wisdom of community members participating in, leading, and benefitted by the research. The Governance Circle ensures that self-determination and self-governance is realized through Indigenous health research; research responsive to community-identified priorities, leadership, control, approval, and community ownership. The Collective Governance embraces ethical, respectful, and reciprocal research through a shared process to address health equity for urban Indigenous Peoples. We share insights and recommendations on how to support meaningful urban Indigenous-led community health 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 | 0.127 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.061 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".