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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".