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Record W2950039849 · doi:10.1108/jcrpp-03-2019-0017

Developing a risk/need assessment tool for women offenders: a gender-informed approach

2019· article· en· W2950039849 on OpenAlexaffabout
Kaitlyn Wardrop, Kayla A. Wanamaker, Dena Derkzen

Bibliographic record

VenueJournal of Criminological Research Policy and Practice · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsGovernment of Canada
Fundersnot available
KeywordsCriminal justicePsychologyRisk assessmentRisk management toolsOriginalityIntervention (counseling)Clinical psychologyApplied psychologySocial psychologyPsychiatryCriminology

Abstract

fetched live from OpenAlex

Purpose Recently, correctional agencies have argued that there are differences between factors influencing men and women’s involvement in the criminal justice system. The purpose of this paper is to examine the validity of a gender-informed risk/need assessment tool. Design/methodology/approach The sample consisted of 620 women offenders admitted to federal Canadian custody, as well as a matched-comparison group of 647 men. Items were selected from routine assessments in an administrative database based on an extensive literature review of factors related to criminal risk for women. Findings Results showed that the items included in this assessment and the overall rating of risk/need significantly predicted any return to custody for both women and men. As well, ratings incrementally predicted any return to custody over and above other established tools. Practical implications The gender-informed risk/need assessment tool, informed by the literature, performed well for both men and women. The research highlights the complementary, not competing, perspectives of gender-neutral and gender-responsive risk and need factors. Originality/value Factors commonly considered gender-salient predicted risk for men and women. The present study demonstrates that risk assessments tools for men and women should look beyond the factors routinely assessed in the research to identify novel dynamic factors that contribute to risk for men and women and could be targeted for intervention.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.015
metaresearch head score (Gemma)0.067
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.941

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.067
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.470
GPT teacher head0.562
Teacher spread0.092 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreMethods · Empirical

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

Citations12
Published2019
Admission routes2
Has abstractyes

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