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Record W4392008485 · doi:10.5038/1911-9933.17.2.1938

The Social Determinants of Health and Genocide: Towards a Public Health Integrated Framework of Genocide and Mass Violence

2023· article· en· W4392008485 on OpenAlexaffvenueabout
Sian Persad, Cheng Xu

Bibliographic record

VenueGenocide Studies and Prevention · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGenocidePublic healthCriminologyPolitical scienceSociologyMedicineLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.202
GPT teacher head0.502
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes3
Has abstractyes

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