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Record W1990239108 · doi:10.3138/cjccj.45.4.405

Does the Relationship Between Family Structure and Delinquency Vary According to Circumstances? An Investigation of Interaction Effects

2003· article· en· W1990239108 on OpenAlexaffvenueabout
Christopher A. Kierkus, Douglas Baer

Bibliographic record

VenueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénale · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsJuvenile delinquencyPsychologyDevelopmental psychologySocioeconomic statusLogistic regressionSocial psychologyDemographySociologyMedicinePopulation

Abstract

fetched live from OpenAlex

Empirical research has shown that familial disruption is associated with delinquent behaviour. Recent investigations suggest that reduced levels of attachment in non-traditional families may be responsible for this effect. However, it is not known whether the impact of familial disruption varies according to familial socio-economic status (SES) or the gender of the children. Some authors have argued that the criminogenic influence is greater for boys, while others have maintained that girls are more adversely influenced. Similar contradictory evidence has been reported with respect to SES. Finally, a substantial number of studies have shown that the influence of familial disruption is largely invariant to gender and SES. The goal of this study was to determine whether or not familial disruption interacts with these two variables. Multivariate logistic regression was used in the investigation. A representative sample of Ontario school children was analysed (N = 1,891). The analysis reveals that family structure interacts with SES, but only with respect to one form of delinquent behaviour. This result may represent a chance finding. Overall, the relationship between family structure and delinquency is remarkably similar across circumstances.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.319
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.165
GPT teacher head0.370
Teacher spread0.206 · 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.

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

Citations18
Published2003
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Criminology and Criminal Justice/La Revue canadienne de criminologie et de justice pénaleSame topicCrime Patterns and InterventionsFrench-language works237,207