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Record W4405919894 · doi:10.1177/08862605241305150

Exploring the Associations Between Community Gun Violence Exposure and Adolescent Delinquency: Evidence from the Future of Families and Child Wellbeing Study

2024· article· en· W4405919894 on OpenAlexaff
Fei Pei, Xiaomei Li, Luyao Kang, Chenming Wang

Bibliographic record

VenueJournal of Interpersonal Violence · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsJuvenile delinquencyPoison controlSuicide preventionHuman factors and ergonomicsInjury preventionOccupational safety and healthGun violencePsychologyMedical emergencyCriminologyMedicine

Abstract

fetched live from OpenAlex

In the past decades, an increasing body of research has delved into the mechanisms of adolescent delinquency from various perspectives, including individual characteristics, interpersonal relationships, school environments, and community settings. However, limited research focused on its association with community gun violence exposure. Utilizing data from 3,595 adolescents ( M = 15.63, SD = 0.71) and their families, we examined how the number of gun violence incidents proximal to adolescents’ homes and schools was linked with their self-reported delinquent behaviors, controlling for other important individual, interpersonal, and community-level predictors of adolescent delinquency. Results revealed relationships between gun violence within 1,000 and 500 m of homes (but not schools) and adolescent delinquency; yet the direction of the relationship differs by distance.

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)0.003
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 score0.081
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.123
GPT teacher head0.365
Teacher spread0.242 · 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
Published2024
Admission routes1
Has abstractyes

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