Victimization and Repeat Victimization Over the Life Span: A Predictive Study and Implications for Policy
Bibliographic record
Abstract
This study was based on the 1999 General Social Survey, a national Canadian survey of criminal victimization involving about 26,000 individuals, 15 years of age and over. More than half of all respondents (57.8%) reported experiencing at least one criminal incident during their life span. More than one in eight (13.5%) were victimized more than once and these repeat victims experienced over half (54%) of all offences. Less than 5% of the sample was victimized three times or more, although these individuals experienced nearly a quarter of all offences reported by the respondents. Logistic regression analyses, relating to violent, property, and all offences, revealed that the variables that best predicted victimization and repeat victimization were age, province of residence, and education, while gender, ethnicity, country of birth, urban residence, and routine activities were less consistent in their ability to predict victimization as a whole or repeat victimization. Taken together, the predictors achieved modest success in predicting membership in the victim and non-victim groups. The study concluded that the concentration of victimization warranted victim-based preventive measures, with the qualification that nearly half of all victimizations were not experienced by repeat but, rather, single-incident victims. It was also recommended that special attention be accorded in the future to understanding the relatively low level of lifetime victimization of persons 65 years of age and over, the elevated risk faced by residents of British Columbia, and the risks of violence faced by Aboriginal Canadians. The study concludes with a call for the use of alternative methodologies to study victimization and for the validation of the General Social Survey through a smaller number of face-to-face interviews in order to ascertain the role played by recall and disclosure issues in victimization surveys involving telephone interviews.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".