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Record W1575651828 · doi:10.1002/9781118314876.ch13

Treatment for Adult Sex Offenders

2013· other· en· W1575651828 on OpenAlexaff
Marnie E. Rice, Grant T. Harris

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsQueen's University
Fundersnot available
KeywordsRecidivismFrequentist inferencePsychologyNull hypothesisSex offenderRandomized controlled trialRandomized experimentCriminologyClinical psychologyBayesian probabilityMedicineBayesian inferenceComputer scienceStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter assures readers that treatment for adult sex offenders has been demonstrated to cause lowered recidivism among adult sex offenders, and that the demonstration has met accepted criteria for the rejection of the relevant null hypothesis. The chapter asks whether, according to the general principles put into research practice, the available evidence warrants accepting these firm conclusions, specifically as they apply to adult sex offenders. The discussion here is based mostly on frequentist statistical reasoning that entails the traditional methodologies of hypothesis testing and confidence intervals. The chapter also considers problems that researchers encounter when they find evidence running counter to the beliefs of other researchers, practitioners, the public, and even themselves. Finally, it outlines a randomized controlled trial (RCT), which the author believes is ethical and feasible for the evaluation of androgen disrupting therapy (ADT).

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.002

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 head0.042
GPT teacher head0.335
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations20
Published2013
Admission routes1
Has abstractyes

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