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Record W2617321752 · doi:10.1177/1541204017710315

The Victim–Offender Relationship and Police Charging Decisions for Juvenile Delinquents

2017· article· en· W2617321752 on OpenAlexaffabout
Heather Rollwagen, Joanna C. Jacob

Bibliographic record

VenueYouth Violence and Juvenile Justice · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
Fundersnot available
KeywordsJuvenile delinquencyOddsPsychologyCriminologyLogistic regressionHuman factors and ergonomicsPoison controlPopulationJuvenileSuicide preventionInjury preventionSocial psychologyMedical emergencyDemographyMedicineSociology

Abstract

fetched live from OpenAlex

While research has established how victim–offender relationship (social distance) relates to police decision-making, comparatively little research has examined this relationship among juvenile delinquents. This article examines how the social relationship between victim and offender has a main and moderating relationship with police charging decisions among juvenile delinquents in Canada. Incidents recorded using the Uniform Crime Reporting Incident-Based Survey ( N = 130,090) are modeled using logistic regression to predict the odds of police laying a charge. Independent variables include nature of the victim–offender relationship as well as demographic, geographic, and offense-specific variables. Main effects models show that incidents involving current intimate partners are most likely to result in arrest, followed by incidents involving strangers. Importantly, stratified models suggest that social distance conditions how other legal and extralegal factors relate to police arrest decisions. Similar to the adult offending population, victim–offender relationship shapes the way criminal incidents are officially addressed in complex ways.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.129
GPT teacher head0.398
Teacher spread0.268 · 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 designQualitative
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

Citations8
Published2017
Admission routes2
Has abstractyes

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