Indigenous Peoples and Type 2 Diabetes: A Discussion of Colonial Wounds and Epistemic Racism
Bibliographic record
Abstract
Racism is rooted in historic and ongoing colonial strategies designed to erase, silence, and dismiss Indigenous peoples' voices, personhood, and worldview. Although within health care today interpersonal racism (discriminatory treatment) is commonly reported on, racism also influences our understanding of health conditions and related treatments. Epistemic racism, the discrimination of how we know, operates through the questions we ask to advance our evidence, and whose knowledge is sought and deemed valid. Epistemic racism is a colonial mechanism that marginalizes and diminishes the power of Indigenous peoples' voices and knowledge bases. In this work, we begin by sharing 2 stories of Indigenous peoples and type 2 diabetes (T2D) from an Indigenous knowledge base and a biomedical knowledge base. Our discussion of epistemic racism, which underlies reported T2D health disparities among Indigenous peoples, includes providing examples of knowledge emerging when the dominance of the biomedical knowledge base is disrupted through centring Indigenous knowledge and peoples. Indigenous-led research, in respectful relations with biomedical worldviews, is imperative. Unsilencing Indigenous peoples' voices and knowledge is necessary when addressing identified T2D health disparities and is truly a health priority. Indigenous revitalization, that is, acceptance of Indigenous knowledge bases, is valid and vital to health and well-being---it is time for ReconciliACTION.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.046 | 0.094 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".