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Record W4231412999 · doi:10.22215/etd/2015-10638

Child Abuse, Its Aftermath, and Criminal Recidivism in a Mixed Gender Sample of Adolescent Offenders

2015· dissertation· en· W4231412999 on OpenAlexaff
Kayla A. Wanamaker

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsCarleton University
Fundersnot available
KeywordsRecidivismPsychologyCriminal justiceSubstance abuseAnxietyClinical psychologyPsychiatryCriminology

Abstract

fetched live from OpenAlex

The current study examines if childhood abuse increases the likelihood of criminal recidivism through four potential mediators-deviant peers, substance abuse, running away from home, and internalizing behaviours (i.e., anxiety, depression), and determine if these relationships vary across gender.To test these relationships, a prospective research design was used with 332 justice-involved youth (113 females, 219 males; ages 12-21).Official provincial recidivism data was obtained and the average follow up time was 30.43 months (SD = 4.96).Overall, substance abuse and deviant peers significantly and positively mediated the relationship between childhood abuse and criminal recidivism for males only.Internalizing behaviours and running away from home, however, were not found to mediate the relationship between child abuse and criminal recidivism for either gender.Limitations include reliance on a simplistic measure of childhood abuse.Results suggest that an integrated approach of combining genderneutral and gender-specific theories of criminal behaviour is warranted.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.326
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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