“Years ago”: reconciliation and First Nations narratives of tuberculosis in the Canadian Prairie Provinces
Bibliographic record
Abstract
For First Nations tuberculosis (TB) patients in the Prairie Provinces, the past matters. In this paper, we draw on the analysis of historical statements made by 20 First Nations interviewees with infectious TB to explore the function of talking about the past in relation to a current diagnosis of TB and the implications of historicity on contemporary TB prevention, programming and care. Despite interviewees not being asked directly about past contexts of TB treatment, they talked about historical topics such as the removal of First Nations TB patients from communities for treatment in distant sanatoria, painful and invasive surgical procedures once used to treat TB, and the attitudes that persist due to the ongoing failure to eliminate TB from First Nations communities. In these narratives, past experiences of TB treatment are intimately connected to present-day experiences and context. What happened ‘years ago’ profoundly affects the health and well-being of people diagnosed with TB today. Attempts to eliminate TB among First Nations peoples in Canada must also address its historical legacy. Understanding the contemporary effects of past TB treatment and mistreatment among First Nations peoples in the Prairie Provinces can also be seen as part of a larger project of truth and reconciliation in Canada, which involves both Indigenous and non-Indigenous Canadians.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.063 | 0.044 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".