Stuck at the Front Door: Gender, Fear of Crime and the Challenge of Creating Safer Space
Bibliographic record
Abstract
This paper is in respectful challenge to two streams in urban social geography and planning literatures: the question of how gendered geographies of fear help constitute identities spatially, and the related question of how gendered urban space can be made and remade to be more egalitarian. I argue that the first of these bodies of literature is often trapped in an unhelpful public–private divide, which reflects the inability of mainstream crime prevention to include violence committed within families and households as a central focus of concern. I further argue that the question of how urban space can become more egalitarian needs to be concerned with violence and fear in the private realm as well as the public realm. Although the paper is primarily a review of recent academic and policy-oriented literature, my arguments are illustrated by a research project on how grassroots organizations serving new-arrival women in the outer suburbs of Melbourne and Toronto, as well as their funders, are redefining violence and safer space.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".