The use of realist approaches for health research in Indigenous communities
Bibliographic record
Abstract
Research approaches and underlying epistemologies should be carefully considered when conducting health research involving Indigenous communities in order to be aligned with the distinct Indigenous values and goals of the communities involved. If Western research approaches are used, it is helpful to consider how they might be consistent with Indigenous ways of knowing. Among Western research approaches, realist approaches might have some congruence with Indigenous epistemologies. For health research in Indigenous communities, realist approaches might be relevant because they are based on a wholistic approach congruent with Indigenous ontologies, anchored in local knowledge, process-oriented and dynamic. The use of these approaches might make it possible to link diverse knowledge systems into action that is meaningful for Indigenous communities.
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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 | 0.265 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.011 | 0.094 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".