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Record W4386820092 · doi:10.1177/21533687231202049

Indigenous Peoples’ Relative Risk of Homicide in the USA: A Systematic and Meta-Analytic Review

2023· article· en· W4386820092 on OpenAlexaff
Amy M. Alberton, Grace K. Hawks, Naomi Williams, Kevin M. Gorey

Bibliographic record

VenueRace and Justice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHomicide, Infanticide, and Child Abuse
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsIndigenousHomicideCriminologyGenocideOppressionPopulationEthnic groupSociologyPoison controlPolitical scienceGeographySuicide preventionDemographyLawMedicinePoliticsEcologyEnvironmental health

Abstract

fetched live from OpenAlex

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.042
GPT teacher head0.333
Teacher spread0.291 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2023
Admission routes1
Has abstractyes

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