Instructions have been provided: Addressing Indigenous-Specific Racism and Implementing Foundational Obligations to Indigenous Peoples
Bibliographic record
Abstract
Introduction: Growing calls to recognize racism as a determinant of health underscores the need for evidence-based tools addressing Indigenous-specific racism in health services, particularly within public health. Indigenous resistance to genocide and assimilation in Canada has led to the current era of truth and reconciliation. Since 2015, a series of national and regional commissions and inquiries have led to the adoption of the UN Declaration on the Rights of Indigenous Peoples into law, as well as over 400 recommendations for actions to eradicate Indigenous-specific racism. We call these our “Foundational Obligations to Indigenous Peoples.” This study introduces a tool designed to measure and address these Foundational Obligations, aiming to confront the unmet responsibility public health has to eradicate Indigenous-specific racism. Methods: Within BC’s Office of the Provincial Health Officer (OPHO), we developed a mixed-method tool “Action on Foundational Obligations to Indigenous Peoples Self-Assessment," where participants quantitatively assess their engagement with reports and recommendations related to Indigenous rights and reconciliation. Qualitative insights delve into the reasoning behind rankings, identifying barriers, facilitators, and support needs for upholding Foundational Obligations. Results: The baseline survey in June 2022 revealed substantial variability in awareness and action across the OPHO. Seven pragmatic themes emerged from qualitative data that inform the work to arrest white supremacy & Indigenous-specific racism. Knowledge Translation: To address the gaps in knowledge, a workshop series— 'Foundational Obligations Series'— was created to educate staff on essential reports, acts, and inquiries that offer clear guidance for addressing both Indigenous-specific racism and white supremacy.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.176 | 0.050 |
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".