Using a metaphor of baskets and copper pots to identify “what work, whose work” in truth, rights, responsibilities, and reconciliation in public health
Bibliographic record
Abstract
Ten years since the Truth & Reconciliation Commission Report, Canadian institutions-including public health systems-have yet to advance the Calls to Action in a sustained, transformative way. As public health leaders in the territory now known as British Columbia, we witness tension as colleagues grapple with, "What is the work of Truth & Reconciliation? Whose work is it?". Too often, truth and reconciliation is delegated to a small Indigenous team (or, individual) dangling, isolated off the side of an organizational chart. We offer a metaphor highlighting two interconnected, but distinct areas of work to advance truth and reconciliation in public health. One is the work of reclaiming and resurgence of languages, culture, medicines, and connection to territory, undertaken by and for First Nations, Inuit, and Métis Peoples. The other is eradicating Indigenous-specific racism and white supremacy to advance cultural safety. It is not up to Indigenous people to eradicate racism; as it is constructed, maintained, and perpetuated by settlers, settlers are those with the power to eradicate it. As we move towards the anniversary of the TRC, we share a metaphor that helps our settler colleagues understand and claim their responsibility in truth, rights, and reconciliation in public health.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.028 | 0.112 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".