Promoting self-determined Indigenous data governance in Canada: the Métis Health Research and Data Governance Principles
Bibliographic record
Abstract
Population-level data collection is crucial to advance Indigenous rights and sovereignty but requires localized approaches to develop representative datasets. In Canada, a focus on First Nations research and data governance and principles has led to the underrepresentation of Métis realities and a reliance on data governance models that fail to address their unique cultural, historical, and community-specific needs. "The Saskatchewan Métis Health Research and Data Governance Principles©" were developed to guide Métis research and promote Métis data sovereignty. While these principles share similarities with the First Nations Principles of OCAP®, they emphasize Métis-specific priorities such as capacity building and active engagement with Métis rights holders. These principles provide a framework for Métis health research, ensuring that Métis values and perspectives are embedded throughout the research lifecycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.165 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.022 | 0.030 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".