Walking the Path Together: Indigenous Health Data at ICES
Bibliographic record
Abstract
Indigenous data governance principles assert that Indigenous communities have a right to data that identifies their people or communities, and a right to determine the use of that data in ways that support Indigenous health and self-determination. Indigenous-driven use of the databases held at the Institute for Clinical Evaluative Sciences (ICES) has resulted in ongoing partnerships between ICES and diverse Indigenous organizations and communities. To respond to this emerging and complex landscape, ICES has established a team whose goal is to support the infrastructure for responding to community-initiated research priorities. ICES works closely with Indigenous partners to develop unique data governance agreements and supports processes, which ensure that ICES scientists must work with Indigenous organizations when conducting research that involves Indigenous peoples.
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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 | 0.130 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.005 | 0.044 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".