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 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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".