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Record W2166633284 · doi:10.1177/1466802503003001453

Perceived collective efficacy and women's victimization in public housing

2003· article· en· W2166633284 on OpenAlexaboutno aff
Walter S. DeKeseredy, Shahid Alvi, E. Andreas Tomaszewski

Bibliographic record

VenueCriminal Justice · 2003
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCollective efficacyPovertyInformal social controlPublic housingDomestic violenceSocial controlCriminologyControl (management)PsychologyPolitical scienceDemographic economicsSuicide preventionPoison controlEconomic growthSocial psychologyEnvironmental healthEconomicsMedicine

Abstract

fetched live from OpenAlex

Although it has not yet been applied to domestic violence and other types of crime in Canadian public housing, the social disorganization/collective efficacy model described in this article may help explain why people who live in such areas characterized by poverty and joblessness report higher rates of intimate partner violence and several other offenses than those living in more affluent communities. Using data generated by the Quality of Neighborhood Life Survey, a main objective of the Canadian study described here was to test this model. One of the most important findings is that community concerns about street crimes and informal means of social control designed to prevent such harms are not effective forms of alleviating intimate partner violence in public housing.

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)0.001
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 score0.288
Threshold uncertainty score0.573

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.082
GPT teacher head0.392
Teacher spread0.309 · 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

Citations65
Published2003
Admission routes1
Has abstractyes

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