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Indigenous Peoples, Criminology, and Criminal Justice

2018· article· en· W2898469334 on OpenAlexaboutno aff
Chris Cunneen, Juan Tauri

Bibliographic record

VenueAnnual Review of Criminology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousColonialismCriminologyCriminal justicePoliticsHarmPolitical scienceSociologyEconomic JusticeLawEcology

Abstract

fetched live from OpenAlex

This review provides a critical overview of Indigenous peoples’ interactions with criminal justice systems. It focuses on the experiences of Indigenous peoples residing in the four major Anglo-settler-colonial jurisdictions of Australia, New Zealand, Canada, and the United States. The review is built around a number of key arguments, including that centuries of colonization have left Indigenous peoples across all four jurisdictions in a position of profound social, economic, and political marginalization; that the colonial project, especially the socioeconomic marginalization resulting from it, plays a significant role in the contemporary over-representation of Indigenous peoples in settler-colonial criminal justice systems; and that a key failure of both governments and the academy has been to disregard Indigenous peoples responses to social harm and to rely too heavily on Western theorizing, policy, and practice to solve the problem of Indigenous over-representation. Finally, we argue that little will change to reduce the negative nature of Indigenous–criminal justice interactions until the settler-colonial state and the discipline of criminology show a willingness to support Indigenous peoples’ desire for self-determination and for leadership in the response to the social harms that impact their communities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
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 head0.059
GPT teacher head0.374
Teacher spread0.315 · 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 designNot applicable
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

Citations134
Published2018
Admission routes1
Has abstractyes

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