Partnerships for women’s safety in the city: “four legs for a good table”
Bibliographic record
Abstract
Ten years after the first Reclaim the Night marches in the late 1970s began to galvanize women around the right to move freely in public and private space without fear of violence, a local governance-based movement to promote women’s safety developed in European and Canadian cities and was later diffused to Africa, Asia and Latin America. This movement drew on urban planning and design as a means to promote women’s empowerment. Partnerships developed around a framework we have titled “four legs for a good table”: community advocates to push for change; local politicians to galvanize government resources; “femocrats” to capture local policies and programmes for emancipatory ends; and researchers to gather evidence around the problem and to document efforts around solutions. This paper traces the collective history of this loosely coordinated movement. Focusing on three case studies, we mark the advancements of theoretical frameworks and practical tools as the women’s safety movement internationalized, and reflect on achievements and challenges.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.028 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".