Trends in Fear of Crime in a Western Canadian City: 1984, 1994, and 2004
Bibliographic record
Abstract
Criminologists have shown much interest in the distribution, causes, and consequences of fear of crime, but few studies have examined trends in fear. Using data from the Winnipeg Area Study from 1984, 1994, and 2004, and official crime data from the Winnipeg Police Service, we examine trends in fear of crime and compare them to reported crime. Fear of crime is evaluated by using an index compiled from five offence-specific indicators that asks how worried people are about becoming victims of theft, burglary, armed robbery, fraud, and sexual assault. Bonferonni procedures and regression methods are used to assess differences in fear of crime. The results show that respondents report low levels of fear of crime over the 20-year period. The results also indicate a lack of correspondence between fear of crime and official measures of crime. These findings challenge the use of fear of crime measures by policy makers seeking to evaluate criminal justice initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".