MétaCan
Menu
Back to cohort
Record W2143103551 · doi:10.1177/0022427809341940

Household Structure, Coupling Constraints, and the Nonpartner Victimization Risks of Adults

2009· article· en· W2143103551 on OpenAlexafffundabout
Carolyn Yule, Elizabeth Griffiths

Bibliographic record

VenueJournal of Research in Crime and Delinquency · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of TorontoNational Science Foundation
KeywordsVulnerability (computing)PsychologyHuman factors and ergonomicsDevelopmental psychologySpace (punctuation)Injury preventionSuicide preventionSocial psychologyPoison controlEnvironmental healthComputer securityMedicine

Abstract

fetched live from OpenAlex

Victimization studies consistently find that household structure influences the risk of personal and property victimization among adult household members, with those in “traditional” homes enjoying the most protection from victimization and lone parents experiencing the greatest vulnerability. Drawing on the concept of coupling constraints , which represents space-time limitations on adults’ routine activities, this study builds upon and extends research on the household structure— victimization relationship by considering how the presence and age of children shapes adult victimization risk. Data from 11,952 urban respondents in the Canadian General Social Survey (1999) confirm that adults’ life course stage, captured in age-graded responsibilities to children, has an independent and direct influence on nonpartner victimization. The heightened victimization risk experienced by lone parents relative to other types of households is largely explained by their parental coupling constraints.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.242
GPT teacher head0.501
Teacher spread0.259 · 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

Citations2
Published2009
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Research in Crime and DelinquencySame topicCrime Patterns and InterventionsFrench-language works237,207