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Record W75119750

Remorse, psychopathology, psychopathic characteristics, and recidivism among adolescent offenders

2013· dissertation· en· W75119750 on OpenAlexfundno aff
Andrew Spice

Bibliographic record

VenueSummit (Simon Fraser University) · 2013
Typedissertation
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
FundersSimon Fraser University
KeywordsRemorseShameRecidivismPsychologyPsychopathyExternalizationPsychopathologyBlameClinical psychologyIncremental validityAngerConstruct validitySocial psychologyPersonalityPsychometrics
DOInot available

Abstract

fetched live from OpenAlex

Remorse has long been considered important to the juvenile justice system. However, the nature of this construct has not yet been clearly articulated, and little research has examined its associations with other theoretically and legally relevant variables. The present study was intended to address these issues by examining relationships among remorse, psychopathology, psychopathic characteristics, and recidivism in a sample of adolescent offenders (N = 97) using the theoretically and empirically established framework of guilt and shame (Tangney & Dearing, 2002). Findings indicated that guilt was negatively related to recidivism, psychopathic characteristics, anger problems, depression, and anxiety. Furthermore, guilt provided incremental validity beyond established risk factors for offending and existing measures of “remorse” in the prediction of recidivism. In contrast to guilt, shame was positively related to recidivism, behavioural features of psychopathy, and numerous mental health problems. Moreover, the externalization of blame that is considered an important feature of shame provided incremental validity in the prediction of recidivism beyond established risk factors for offending as well as existing measures of “remorse”. These results suggest that assessment of guilt, shame, and externalization of blame may be of greater utility than “remorse”, and also underscore these features as potentially important treatment targets for adolescent offenders.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.255
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

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
Published2013
Admission routes1
Has abstractyes

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