MétaCan
Menu
Back to cohort
Record W3175240144 · doi:10.35502/jcswb.181

Comparing the lifestyles of victims: A routine activity theory assessment of repeat victimization in Canada

2021· article· en· W3175240144 on OpenAlexvenueaboutno aff
Zavin Nazaretian, Chivon H. Fitch

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMultinomial logistic regressionLogistic regressionPsychologyLesbianRace (biology)Socioeconomic statusSocial psychologyDemographySociologyMedicineGender studiesStatistics

Abstract

fetched live from OpenAlex

This paper simultaneously explores the relationship between social status, routine activity theory, and repeat victimization. This study compares the effects of lifestyle with key social status variables like gender, race, and sexuality, on varying degrees of victimization to answer the question: do routine activities or social status predict repeat victimization? This research is a secondary data analysis using two waves of the Canadian Victimization Survey from 2004 and 2009. Both a logistic regression and multinomial logistic regression are used to analyze the possible causes of repeat victimization. Overall, social status is influenced by lifestyle when predicting victimization; however, key social status variables predict high levels of victimization such as identifying as gay or lesbian or being an Aboriginal Canadian. The most powerful indicator of victimization was if a victim had been previously arrested themselves. The results of this study suggest that, while lifestyle is a strong predictor of victimization, minority groups are still at risk of being victimized at higher levels.

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)0.003
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 score0.026
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.022
GPT teacher head0.311
Teacher spread0.289 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Community Safety and Well-BeingSame topicSexual Assault and Victimization StudiesFrench-language works237,207