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Record W2169017908 · doi:10.1177/0044118x09336631

The Relationship Between Adolescents’ Experience of Family Violence and Dating Violence

2009· article· en· W2169017908 on OpenAlexaff
Lise Laporte, Depeng Jiang, Debra Pepler, Claire Chamberland

Bibliographic record

VenueYouth & Society · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsYork UniversityMcGill UniversityUniversité de MontréalCentre Jeunesse de Quebec
Fundersnot available
KeywordsAggressionPsychologyDating violenceIntervention (counseling)Suicide preventionPoison controlDomestic violenceDevelopmental psychologyInjury preventionAgency (philosophy)Human factors and ergonomicsClinical psychologyMedicinePsychiatryMedical emergencySociology

Abstract

fetched live from OpenAlex

This study examines whether experiences of familial victimization and aggression are potential risk factors for dating violence in male and female teenage relationships. The authors compare 471 adolescents aged 12 to 19 in the care of a youth protection agency and from a community sample. Results show that adolescents carry negative childhood experiences of family violence into their intimate relationships in different ways, depending on gender and level of risk. Female adolescents who had been victimized by either of their parents were at greater risk for revictimization, but not aggression, within their dating relationships. High-risk adolescent males who reported childhood victimization were at a particularly high risk of being aggressive toward their girlfriends, especially if they were harshly disciplined by their father. The extent of aggression toward parents predicted aggression toward dating partners, particularly for girls. The authors discuss these findings in terms of prevention and early intervention efforts.

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)0.007
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.351
Teacher spread0.276 · 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

Citations69
Published2009
Admission routes1
Has abstractyes

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