Gender, social desirability, and fear of crime: are women really more afraid?
Bibliographic record
Abstract
Fear of crime influences people’s daily habits (Lavrakas, 1982), as well as entire communities’ feelings of safety and cohesion (Gates, 1987). Gender has been identified as the strongest and most consistent predictor of fear of crime (for a review, see Hale, 1996). The literature consistently finds that women report greater fear of crime than do men. This finding is paradoxical when compared with the concurrent finding that men experience greater criminal victimization than do women. This phenomenon is referred to as the fear victimization paradox (Rennison, 2000). At first, it was accepted that women were more afraid of crime than were men and investigators offered many different theories to explain the paradox (e.g., Fisher & Sloan, 2003, Killias & Clerici, 2000, & Sacco, 1990). However, Sutton and Farrall (2005) investigated the possibility that masculinity was creating a social desirability bias in men’s reporting of fear of crime and when they accounted for this social desirability bias, they found that men actually experienced greater fear of crime than did women. The current investigation replicated and extended this research with 1009 university students and 508 Winnipeg residents. It extended Sutton and Farrall’s study by including measures of fear of crime and social desirability that have greater validity and by testing whether the findings apply differentially to fear of sexual versus non-sexual types of criminal victimization. The influence of age, location of residence within the city of Winnipeg, history of victimization, and masculinity on fear of crime was explored. This investigation was able to replicate Sutton and Farrall’s finding, but only in the community sample. The findings from this investigation suggest that there is a shift as men leave university from actually being less afraid of crime than women to being more afraid of crime. However, despite their increased fear, men in the community seem to maintain the façade of fearlessness. It was also found that women were consistently more afraid of sexual victimization than men, regardless of the influence of social desirability. Masculinity and social desirability had similar negative relationships to fear of crime and the implications of this are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".