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Record W2153724765 · doi:10.1177/0093854813503443

Does Bully Victimization Predict Future Delinquency?

2013· article· en· W2153724765 on OpenAlexaff
Jennifer S. Wong, Matthias Schonlau

Bibliographic record

VenueCriminal Justice and Behavior · 2013
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of WaterlooSimon Fraser University
Fundersnot available
KeywordsJuvenile delinquencyPsychologyPropensity score matchingPoison controlProperty crimeInjury preventionSuicide preventionHuman factors and ergonomicsSchool dropoutMatching (statistics)Dropout (neural networks)CriminologyClinical psychologyDevelopmental psychologyViolent crimeMedicineMedical emergencyDemographic economicsComputer science

Abstract

fetched live from OpenAlex

Over the past decade school bullying has emerged as a prominent issue of concern for students, parents, educators, and researchers. Bully victimization has been linked to a long list of negative outcomes, such as depression, peer rejection, school dropout, eating disorders, delinquency, and violence. Previous research relating bully victimization to delinquency has typically used standard regression techniques that may not sufficiently control for heterogeneity between bullied and nonbullied youths. Using a large, nationally representative panel dataset, the National Longitudinal Survey of Youth 1997 (NLSY97), we use a propensity score matching technique to assess the impact of bully victimization on a range of delinquency outcomes. Results show that 19% of respondents had been victimized prior to the age of 12 years ( n = 8,833). Early victimization is predictive of the development of 6 out of 10 delinquent behaviors measured over a period of 6 years, including assault, vandalism, theft, other property crimes (such as receiving stolen property or fraud), selling drugs, and running away from home. Bully victimization should be considered an important precursor to delinquency.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.694
Threshold uncertainty score0.996

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.299
Teacher spread0.279 · 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

Citations51
Published2013
Admission routes1
Has abstractyes

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