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Record W2097813466 · doi:10.1177/0093854811406356

The Risk-Need-Responsivity (RNR) Model

2011· article· en· W2097813466 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCriminal Justice and Behavior · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of SaskatchewanPublic Safety CanadaCarleton University
Fundersnot available
KeywordsPsychologyPoison controlGeneralized linear modelApplied psychologyComputer scienceMedicineEnvironmental healthMachine learning

Abstract

fetched live from OpenAlex

The risk-need-responsivity (RNR) model has been widely regarded as the premier model for guiding offender assessment and treatment. The RNR model underlies some of the most widely used risk-needs offender assessment instruments, and it is the only theoretical model that has been used to interpret the offender treatment literature. Recently, the good lives model (GLM) has been promoted as an alternative and enhancement to RNR. GLM sets itself apart from RNR by its positive, strengths-based, and restorative model of rehabilitation. In addition, GLM hypothesizes that enhancing personal fulfillment will lead naturally to reductions in criminogenic needs, whereas RNR posits the reverse direction. In this article the authors respond to GLM’s criticisms of RNR and conclude that little substance is added by GLM that is not already included in RNR, although proponents of RNR may learn from the popular appeal that GLM, with its positive, strength-based focus, has garnered from clinicians over the past decade.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.851

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.098
GPT teacher head0.346
Teacher spread0.248 · 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