Peace is essential prerequisite of health: linkage in Canadian Indigenous peoples.
Bibliographic record
Abstract
Recently I completed teaching a 12-week course on Peace through Health for Canadian undergraduate students. During the course they had been moved by the lives of children in Gulu, Northern Uganda, had considered medicine and peace actions in Sri Lanka, Afghanistan, Croatia, El Salvador, and more. Their final examination was intended to bring them right back home to the invisible conflict under our noses in Canada. The exam paper began with this scenario: You are part of a health team working in a rural area. In this area, 95% of inhabitants are indigenous people. They are a minority in most of the country and have been so since it was settled by Europeans in the 17th century. For the indigenous people, the next three centuries were a history of violence, expropriation of land, and attempted extinction of culture through a residential school system. The health of this population is much worse than in the majority population, measured by all indicators. Their housing, water system, sanitation, and school infrastructure are all substandard compared to the rest of the population. Unemployment is high. A nearby mining development employs very few of these people, although the land was expropriated from them with an agreement for employment opportunities. Recently, a nonviolent land claims demonstration elicited a massive police response in which two indigenous people were shot dead. You are aware, from the youth who come to your clinic, that there is serious unhappiness and hopelessness among them and an attraction to guns and violence. Accident, homicide, and suicide rates are very high among youth. The students were then asked to identify the health deficits and the peace deficits. They had then to generate ideas for health sector action on both health and peace. They had to work out the intellectual and ethical foundations of an intervention and how to evaluate it. Interestingly, while most students realized we were talking about Canada in this scenario, there was a proportion that imagined we were discussing a problem in a far away country! The scenario does indeed generalize to other indigenous populations. However, it is easier to see a speck of dirt on another’s shirt than the big, ugly stain on one’s own.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.056 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".