The Social Determinants of Health and Genocide: Towards a Public Health Integrated Framework of Genocide and Mass Violence
Bibliographic record
Abstract
This paper makes a normative argument about transformations of public health as a necessary condition required in any transitional justice process. We seek to bridge the gap between the fields of genocide and public health to understand the recursive relationship between genocide and the social determinants of health. We show that structures and institutions established during genocide create enduring impacts on the public health outcomes of victim and survivor groups even after the ousting of the original perpetrators. Our comparative analysis of the Rwandan Genocide and the colonial genocide of Indigenous communities in Canada surveys the available public health literature and argues that perpetrators of genocide deliberately design public health systems for the explicit purposes of destroying target communities over the longue durée. When these systems are insufficiently transformed, post-genocide societies face significant barriers to transitional justice and reconciliation as a direct result of their impacts on survivor communities. In Rwanda, delayed addressal of the HIV/AIDS epidemic engineered by the Hutu Power regime continued to victimize Tutsi women decades after the mass killings have ended; in Canada, legacies of family separation and the Indian Residential School system have straddled Indigenous communities with high rates of comorbidities and early death consistent with colonial genocide policies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".