Participatory Planning with Women for Everyday Life and Safety: the Case of Madrid
Bibliographic record
Abstract
Safety in public space and transportation systems is one key aspect to consider when integrating gender dimensions into city planning and design. Research undertaken since the 1970s has shown how spatial features have an impact on both subjective perceptions of fear and actual probabilities of crime. This evidence has led to the development of various methodologies for addressing crime prevention through environmental design. Some of these have been developed from a feminist standpoint and are based on participatory methods with local women called urban safety audits and also exploratory safety walks. The original methodology was developed in Canada in the early 1990s and it included six principles for safe urban space. This article shows the results of applying a modified version of the safety audits actualized to current conditions in Global North cities. These safety audits were conducted as part of the design process of a big tract of underused land called Madrid Nuevo Norte. Madrid Nuevo Norte is probably the biggest ongoing redevelopment project in Europe. It is also the first one to systematically integrate a gender dimension in its design. The audits were designed and implemented by the UNESCO Chair on Gender.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".