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DIFFERENTIAL COERCION, STREET YOUTH, AND VIOLENT CRIME*

2009· article· en· W2094362948 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

VenueCriminology · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsQueen's University
Fundersnot available
KeywordsCoercion (linguistics)AngerPsychologyStructural equation modelingSocial psychologyCriminology

Abstract

fetched live from OpenAlex

Using a sample of 300 homeless street youths, this study examines differential coercion theory and the role that coercion and the socialpsychological deficits of anger, low self‐control, coercive modeling, coercive ideation, and control imbalances play in the generation of violent crime. Results from cross‐sectional and prospective offending models that examine the individual mediators reveal that coercion has a direct relationship with violent offending as well as a relationship that is mediated by low self‐control, anger, coercive modeling, and coercive ideation. Although control imbalances have a direct relationship with crime, they do not mediate the relationship between coercion and crime. In the cross‐sectional model that contains all the mediators, coercion, low self‐control, anger, coercive modeling, and coercive ideation are associated with crime. In the prospective model that contains all the mediators, only anger, coercive modeling, and coercive ideation remain associated with crime. Results are discussed regarding future theory development and policy implications.

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)0.000
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 score0.806
Threshold uncertainty score0.999

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)0.0020.000

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 head0.136
GPT teacher head0.368
Teacher spread0.232 · 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