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Record W4213206942 · doi:10.1111/lcrp.12211

Urgent issues and prospects in correctional rehabilitation practice and research

2022· article· en· W4213206942 on OpenAlexaff
Tony Ward, Bruce A. Arrigo, Mary Barnao, Anthony R. Beech, Deirdre A. Brown, Jacinta R. Cording, Andrew Day, Russil Durrant, Theresa A. Gannon, Stephen D. Hart, David S. Prescott, Annalisa Strauss‐Hughes, Armon Tamatea, Faye S. Taxman

Bibliographic record

VenueLegal and Criminological Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCriminal justiceOppressionCriminologyIntervention (counseling)Social workPsychologyClinical social workRehabilitationEconomic JusticeWork (physics)Engineering ethicsMedical educationApplied psychologyPsychiatryMedicinePolitical scienceLawPoliticsEngineering

Abstract

fetched live from OpenAlex

Abstract The aim of this paper is to identify some of the urgent issues currently confronting criminal justice policymakers, researchers and practitioners. To this end a diverse group of researchers and clinicians have collaborated to identify pressing concerns in the field and to make some suggestions about how to proceed in the future. The authors represent individuals with varying combinations of criminal justice research, professional training (e.g. social work, criminal justice, criminology, social work, clinical psychology) and clinical orientation, and experience. The paper is comprised of 13 commentaries and a subsequent discussion based on these reflections. The commentaries are divided into the categories of explanation of criminal behaviour, clinical assessment and correctional intervention, and cover issues ranging from the role of clinical expertise in treatment, problems with risk assessment to the adverse effects of social oppression on minority groups. Following the commentaries, we summarize some of their key themes and briefly discuss a number of major issues likely to confront the field in the next 5–10 years.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.462
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.115
GPT teacher head0.448
Teacher spread0.334 · 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 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

Citations19
Published2022
Admission routes1
Has abstractyes

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