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Record W2015012113 · doi:10.1177/1077559507301843

Understanding the Link Between Childhood Maltreatment and Violent Delinquency: What Do Schools Have to Add?

2007· article· en· W2015012113 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

VenueChild Maltreatment · 2007
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of TorontoWestern University
Fundersnot available
KeywordsJuvenile delinquencyPoison controlPsychologyInjury preventionHuman factors and ergonomicsSuicide preventionDevelopmental psychologyOccupational safety and healthClinical psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

Child maltreatment constitutes significant risk for adolescent delinquency. Although an ecological model has been proposed to explain this relationship, most studies focus on individual risk factors. Prospective data from 1,788 students attending 23 schools were used to examine the additive influence of childhood maltreatment, individual-level risk factors, and school-level variables assessed at the beginning of Grade 9 on delinquency 4 to 6 months later. Individual-level results indicated that being male, experiencing childhood maltreatment, and poor parental nurturing were predictors of violent delinquency. School climate also played a significant role: Given the same individual risk profile, a student attending a school that was perceived by students as safe was less likely to engage in violent delinquency than was a student attending a school perceived to be unsafe. Moreover, the impact of childhood maltreatment on risk for engaging in violent delinquency was somewhat mitigated by schools' participation in a comprehensive violence prevention program.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.001

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 head0.056
GPT teacher head0.310
Teacher spread0.254 · 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