MétaCan
Menu
Back to cohort
Record W3110890553 · doi:10.1177/0093854820977577

The Relationship Between Self-Esteem, Gender, Criminal Attitudes, and Recidivism in a Youth Justice Sample

2020· article· en· W3110890553 on OpenAlexaff
Shreena Thapa, Shelley L. Brown, Tracey A. Skilling

Bibliographic record

VenueCriminal Justice and Behavior · 2020
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsCentre for Addiction and Mental HealthUniversity of TorontoCarleton University
Fundersnot available
KeywordsRecidivismPsychologyPrideSelf-esteemCriminal justicePoison controlSocial psychologyJuvenile delinquencyHuman factors and ergonomicsInjury preventionClinical psychologyDevelopmental psychologyCriminologyMedicinePolitical scienceMedical emergencyLaw

Abstract

fetched live from OpenAlex

The Risk-Need-Responsivity (RNR) model deems criminal attitudes a high-priority criminogenic target for both genders while self-esteem is considered noncriminogenic, hence low priority. In contrast, self-esteem is afforded greater priority among gender-responsive researchers, while the construct of criminal attitudes is afforded lesser priority. We examined whether self-esteem and gender moderated the relationship between criminal attitudes and recidivism among 300 justice-involved youth (200 males, 100 females). Contrary to the hypothesis, high self-esteem (≥72.15th percentile) magnified the relationship between criminal attitudes (Pride in Delinquency Scale) and recidivism in females only; self-esteem levels evidenced no impact on the relationship between criminal attitudes and recidivism among males. Results suggest that prioritizing self-esteem as a treatment target among justice-involved female youth without simultaneously considering whether or not pride in criminal conduct is also present may inadvertently increase reoffending. Implications for exploring whether high self-esteem may in reality represent falsely inflated self-esteem are discussed.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.242
GPT teacher head0.397
Teacher spread0.154 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueCriminal Justice and BehaviorSame topicEvolutionary Psychology and Human BehaviorFrench-language works237,207