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Record W2988176262 · doi:10.1080/14999013.2019.1684404

Age and Strengths in a Community Corrections Sample

2019· article· en· W2988176262 on OpenAlexaff
Caleb D. Lloyd, Bronwen Perley-Robertson, Ralph C. Serin

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton University
Fundersnot available
KeywordsRecidivismPredictive validityPsychologyProsocial behaviorClinical psychologyDemographyDevelopmental psychology

Abstract

fetched live from OpenAlex

Variables conceptualized as strengths are theoretically important for the management of recidivism risk among juveniles and adults. Although measures of strengths are related to recidivism outcomes, little is known about whether these factors may vary in prevalence or predictive validity across age. We examined the predictive validity of strengths among male parolees supervised within community corrections ( N = 3,169) rated by supervision officers. The prevalence of strengths did not differ across age. Further, strengths exhibited a promotive effect across all age groups in that predictive validity was consistent across age, with the exception that strength variables were associated with an enhanced promotive effect among older individuals when predicting violent community outcomes. In particular, prosocial relationships were more strongly related to reduced violent recidivism among older compared to younger parolees. Further research is required to identify factors that may have protective or buffering effects among higher risk younger individuals.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.573

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 science0.0000.000
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 head0.028
GPT teacher head0.379
Teacher spread0.351 · 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

Citations20
Published2019
Admission routes1
Has abstractyes

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