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Record W3207117608 · doi:10.1177/10790632211047185

Improving Our Risk Communication: Standardized Risk Levels for Brief Assessment of Recidivism Risk-2002R

2021· article· en· W3207117608 on OpenAlexaff
Julie Blais, Kelly M. Babchishin, R. Karl Hanson

Bibliographic record

VenueSexual Abuse · 2021
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton UniversityRoyal Ottawa Mental Health CentreUniversity of OttawaDalhousie University
Fundersnot available
KeywordsRecidivismRisk assessmentPsychologyActuarial scienceMeaning (existential)Risk management toolsClinical psychologyRisk analysis (engineering)DemographyMedicineComputer scienceComputer securityBusinessSociology

Abstract

fetched live from OpenAlex

A Five-Level Risk and Needs system has been proposed as a common language for standardizing the meaning of risk levels across risk/need tools used in corrections. Study 1 examined whether the Five-Levels could be applied to BARR-2002R ( N = 2,390), an actuarial tool for general recidivism. Study 2 examined the construct validity of BARR-2002R risk levels in two samples of individuals with a history of sexual offending ( N = 1,081). Study 1 found reasonable correspondence between BARR-2002R scores and four of the five standardized risk levels (no Level V). Study 2 found that the profiles of individuals in Levels II, III, and IV were mostly consistent with expectations; however, individuals in the lowest risk level (Level I) had more criminogenic needs than expected based on the original descriptions of the Five-Levels. The Five-Level system was mostly successful when applied to BARR-2002R. Revisions to this system, or the inclusion of putatively dynamic risk factors and protective factors, may be required to improve alignment with the information provided by certain risk tools.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.366
Teacher spread0.324 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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