Indigenous Peoples’ Relative Risk of Homicide in the USA: A Systematic and Meta-Analytic Review
Bibliographic record
Abstract
Evidence suggests that Indigenous Peoples have the highest rate of death by homicide compared to other ethnic groups in the USA. Despite this alarming disparity and its fatal implications, there seems little attention paid to this crisis outside of Indigenous communities, and literature on the violence perpetrated against this population is comparatively scarce. Among the 574 federally recognized tribes and 326 reservations across the USA, there is great diversity. Yet, Indigenous Peoples share similar experiences of colonialism, genocide, oppression, and marginalization. These experiences highlight how existing social structures and systems continue to function as oppressive forces against Indigenous Peoples. The current study meta-analytically synthesized the existing body of knowledge to summarize current understandings of the relative risk of homicide faced by Indigenous Peoples. Following systematic searches of published and gray literature, data were extracted from 38 eligible studies. As hypothesized, Indigenous Peoples’ risk of homicide was consistently about three times greater than that of others in the USA over the past generation, but counter-hypothetically no gender divide was observed. These findings suggest prevalent, grave and longstanding social-structural and ultimately, health inequities among Indigenous Peoples in the USA. Future research needs and policy implications are discussed.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".