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Record W3034705312 · doi:10.1093/bjc/azaa039

Reducing the Use of Imprisonment: Lessons from 20 years’ experience in Canada

2020· article· en· W3034705312 on OpenAlexaffabout
Andrew A. Reid

Bibliographic record

VenueThe British Journal of Criminology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsDouglas College
Fundersnot available
KeywordsImprisonmentPrisonCriminologySentencePolitical sciencePsychologyComputer science

Abstract

fetched live from OpenAlex

Abstract In order to reduce or constrain prison populations, many different strategies have been proposed, trialled, or implemented. In 1996, Canada created the first and, to date, most ambitious home confinement sanction, the Conditional Sentence of Imprisonment (CSI). This study tracks annual changes to correctional admissions since the introduction of the sanction to assess whether it has reduced custodial admissions for Aboriginal offenders. There is evidence that the CSI had a modest decarceration effect overall and for Aboriginal offenders specifically. These effects were strongest in the initial years after the sanction was introduced, with waning performance in the most recent decade. The decarceration effects have not been erased but nor has the serious problem of over-incarceration among Aboriginal offenders.

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)0.005
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.925
Threshold uncertainty score0.542

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0110.003
Scholarly communication0.0040.001
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.158
GPT teacher head0.325
Teacher spread0.168 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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