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Record W2088179345 · doi:10.1093/bjc/44.1.92

Living in the Shadow of Prison: Lessons from the Canadian Experience in Decarceration

2004· article· en· W2088179345 on OpenAlexaboutno aff
Jenny Roberts

Bibliographic record

VenueThe British Journal of Criminology · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsImprisonmentPrisonSentenceShadow (psychology)CriminologyLife imprisonmentCriminal justicePolitical sciencePsychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

A number of jurisdictions, including England and Wales, have recently experienced rising prison populations and this has renewed the search for fresh alternatives to incarceration. Following the recommendations of the Home Office Sentencing Review of 2001, the Criminal Justice Bill 2002 contains a suspended term of imprisonment, which is a form of conditional sentence for England and Wales. Although conditional sentences of various kinds exist in many Western nations, the Canadian experience carries the most lessons for other jurisdictions. A conditional sentence of imprisonment designed to replace actual terms of custody was introduced in 1996. The offender is sentenced to a term of imprisonment, but discharges the sentence while living in the community, under supervision. As long as they comply with a number of conditions, offenders serving conditional sentences will not actually spend any time in a correctional institution. This paper evaluates the effectiveness of this sanction to date. Results indicate that the conditional sentence has occasioned a significant drop in the number of admissions to custody, and with only a minor degree of net widening. The paper concludes by drawing some general conclusions from the Canadian experience with this alternative to 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 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)0.001
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 score0.226
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.092
GPT teacher head0.352
Teacher spread0.260 · 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

Citations40
Published2004
Admission routes1
Has abstractyes

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