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Record W4402293629 · doi:10.1002/9781119893073.ch18

What Works with Women Who Have Convictions for Sexual Offences

2024· other· en· W4402293629 on OpenAlexaff
Franca Cortoni

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsInternational Centre for Comparative Criminology
Fundersnot available
KeywordsCriminologySexual assaultPsychologyMedicineEnvironmental healthHuman factors and ergonomicsPoison control

Abstract

fetched live from OpenAlex

Viewing women who sexually offend as worse than men who engage in the identical behaviour is akin to how women's criminal behaviour in general has traditionally been viewed. Increased scientific attention demonstrates the need for gender-responsive assessment, treatment and management practices with women who have sexually offended. The majority of the women who sexually offend represent a wide range of individuals such as babysitters, neighbours, teachers, family members, family friends and even childcare workers. Very little research investigated women who sexually offend against adolescent males. Women, like men, also use electronic means for the production of child sexual abuse material. In contrast, gender-responsive practices require an understanding of gender-responsive assessment, treatment and management practices and of both gender-neutral and gender-specific factors that contribute to the sexually offending behaviour. Research consistently shows that women demonstrate much less interests in paraphilic sexual behaviours than men.

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)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0200.004

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 head0.027
GPT teacher head0.326
Teacher spread0.298 · 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 designObservational
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

Citations1
Published2024
Admission routes1
Has abstractyes

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