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Record W2022502181 · doi:10.1177/0886260508314302

Further Investigation of Findings Reported for the Minnesota Sex Offender Screening Tool–Revised

2008· article· en· W2022502181 on OpenAlexaff
Calvin M. Langton, Howard E. Barbaree, Leigh Harkins, Edward J. Peacock, Tamara Arenovich

Bibliographic record

VenueJournal of Interpersonal Violence · 2008
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCentre for Addiction and Mental HealthMinistry of Community Safety and Correctional ServicesUniversity of Toronto
Fundersnot available
KeywordsRecidivismPsychologyReplication (statistics)Sex offenderPoison controlReceiver operating characteristicHuman factors and ergonomicsClinical psychologyMedicineComputer scienceMedical emergencyMachine learning

Abstract

fetched live from OpenAlex

Among a number of widely used risk assessment instruments with adult sexual offenders, the Minnesota Sex Offender Screening Tool-Revised (MnSOST-R) has been subject to relatively few evaluation studies. Only two independent research groups have published replication studies in the peer-reviewed literature with data not provided by the MnSOST-R's developers, and the results regarding the accuracy of predicting sexual recidivism have been mixed. In this article, important differences between the Barbaree et al. and Langton et al. studies are presented. Analyses reported for the various subsets comprising these two samples indicate that coding discrepancies in the Barbaree et al. study account for the different findings, with a moderate level of predictive accuracy using the Receiver Operating Characteristic curve ultimately found for the MnSOST-R in both data sets.

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)0.071
metaresearch head score (Gemma)0.300
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.929
Threshold uncertainty score0.377

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.300
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.006
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.078
GPT teacher head0.319
Teacher spread0.240 · 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.

Study designObservational
DomainMethods
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

Citations6
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Interpersonal ViolenceSame topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207