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Record W2064063049 · doi:10.3138/cjccj.2014.s03

Sentencing, Aboriginal Offenders: Law, Policy, and Practice in Three Countries

2014· article· en· W2064063049 on OpenAlexvenueaboutno aff
Samantha Jeffries, Philip Stenning

Bibliographic record

VenueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsImprisonmentLegislatureCriminal justiceCriminologyRepresentation (politics)LawPolitical scienceEconomic JusticeCriminal lawSociologyPolitics

Abstract

fetched live from OpenAlex

The statistical “over-representation” of Aboriginal people in the criminal justice systems (especially prisons) of Canada, Australia, and New Zealand is not disputed. Sentencing is often perceived as a point in the criminal justice system where, potentially, the problem of Aboriginal over-representation could be addressed. During the last 20 years there have been robust discussions in Canada, Australia, and New Zealand as to whether (and if so how) Aboriginality should be taken into account in sentencing. Reviewing and comparing the trajectories of these debates within the three countries during the last 20 years, in terms of legislative provisions, court decisions, and innovative sentencing practices, suggests that although the problem of over-incarceration is viewed similarly, sentencing responses have varied between nations, but have been equally unsuccessful in actually reducing rates of Aboriginal imprisonment.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.979
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0120.008
Scholarly communication0.0040.002
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.349
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénaleSame topicCriminal Justice and Corrections AnalysisFrench-language works237,207