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Record W3127881258 · doi:10.3138/cjcrim.43.1.123

Making prisons safer and more humane environments

2001· article· en· W3127881258 on OpenAlexaffvenue
Paul Gendreau, David Keyes

Bibliographic record

VenueCanadian Journal of Criminology · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsHumanitiesPolitical sciencePrisonArtLaw

Abstract

fetched live from OpenAlex

Le débat en cours concernant les effets sur les détenus de divers aspects de la vie carcérale est très important, c'est même nécessaire pour que les professionnels de la correction gardent à la mémoire qu'il nous faut au moins rendre l'environnement carcéral sécuritaire et bienfaisant. À cette fin, dans ce texte, nous faisons ressortir les données obtenues dans trois récentes recherches quantitatives: (1) la prévision des inconduites dans la prison, (2) les types de programmes qui aident à réduire l'inconduite, et (3) les méthodes de gestion qui favorisent l'amélioration des conditions de vie des personnes incarcérées et de leur gardiens.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.994
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.006
Scholarly communication0.0060.003
Open science0.0010.005
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0150.001

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 head0.097
GPT teacher head0.337
Teacher spread0.240 · 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

Citations43
Published2001
Admission routes2
Has abstractyes

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Same venueCanadian Journal of CriminologySame topicCriminal Justice and Corrections AnalysisFrench-language works237,207