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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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.078
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 teacher head, 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

Citations6
Published2008
Admission routes1
Has abstractyes

Explore more

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