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Record W4200344686 · doi:10.5964/sotrap.4595

Predictive validity of Stable-2007 in incarcerated samples

2021· article· en· W4200344686 on OpenAlexaff
Jan Looman, Joshua Goldstein, Brian R. Abbott, Jeff Abracen

Bibliographic record

VenueSexual Offending Theory Research and Prevention · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsRecidivismSample (material)PsychologyPredictive validityClinical psychologyRisk assessmentSexual violenceDemographyPsychiatryComputer securityComputer scienceCriminology

Abstract

fetched live from OpenAlex

Some are unclear whether risk assessment instruments, specifically dynamic risk instruments, have demonstrated utility in the risk estimation, treatment recommendations, and monitoring change over time in men at risk for or under sentence of Indeterminate Detention (ID) for sexual offenses. We compare two datasets, the first consisting of individuals representing a routine sample of persons convicted of a sexual offense and the second of men representative of a high risk/needs sample. These two distinct samples (n = 442, mean Static-99R score = 2.4; n = 168, mean Static-99R score 4.5) were then also scored on the Stable-2007. For both groups this scoring occurred in an institutional setting. The Stable-2007 predicted sexual recidivism in Sample 1 independently and in conjunction with the Static-99R. In the high-risk sample the results were the same. In both samples a compound outcome variable (Sexual + Violent reoffense) was also calculated with the Stable-2007 predicting the compound outcome variable in Sample 1 but not Sample 2. This is interesting in that it suggests that the Stable-2007 assesses constructs specific to sexual re-offense in higher risk offenders and not general traits of violence or common anti-social behaviour. Limitations and directions for further research are discussed.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.209
GPT teacher head0.437
Teacher spread0.228 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2021
Admission routes1
Has abstractyes

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