Bringing bodies into planning: Visceral methods, fear and gender violence
Bibliographic record
Abstract
Planning has been ineffective at addressing women’s fear of violence and violence against women in part because of the false public/private divide. This divide is parallel and mutually supported by parochial and conservative understandings of male and female gender constructions and norms in spaces and social structural systems. We propose exploring the actual spaces of bodies and planning at the scale of bodies since bodies are at the nexus of public–private spaces, gender identities and gender violence. Using bodies as geographical spaces to understand and analyse visceral experiences and fear of violence may help diminish the dominance of the public–private divide and challenge the unequal rights women have to use space. Based on exploratory workshops in New York City, Mexico City and Barcelona as well as research events in Medellin, we share our experiences using visceral methods including body-map storytelling and shared sensory spatial experiences, also evaluating their usefulness. We examine the ethics of visceral methods, ways to analyse body-mapped data and the use of planners’ bodies as tools in research and practice. We conclude that bodies have the potential to become a source of dynamic and reflective information that might be effectively used by planners and communities to make places better and safer.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".