Bibliographic record
Abstract

 
 
 As an Indigenous person, I came into the world of Indigenous health scholarship in the 1990s with a personal view that focused on the strength and solutions of our peoples and our cultures. Over the next two decades in research and clinical environments, I observed how biomedicine remained firmly entrenched as the dominant model of care for Indigenous individuals and communities, with traditional knowledges and medicines as an aside or non- existent entirely. I have built my life’s work as a researcher and clinician in centering Indigenous knowledges and healing in both research and health care. Yet today in 2020, biomedicine and Western academic research still dismiss Indigenous knowledges and remain mostly in command of Indigenous health. There are wonderful pockets of Indigenous researchers and practitioners, supported by Indigenous communities that continue to have very little real autonomy or self- determination from colonialism, who are making a difference in Indigenous health by reducing health disparities, using our strengths such as culture, spirituality, medicines, the land, Elders, youth, and more. This issue highlights some of the work by researchers that are making a strong impact on Indigenous health, uplifting our communities.
 
 
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".