Urban Resilience: A Study of Leftover Spaces and Play in Dense City Fabric
Bibliographic record
Abstract
Cities worldwide are urgently moving towards a more resilient and sustainable future. On this quest, national, regional, and local governments apply a combination of socio-spatial tools that regenerate and transform the city’s leftover spaces. There is an abundance of community gardens, cultural centers, and large-scale urban developments that, through programmed activities, reactivate underused spaces. The bearers of this process are professionals and individuals who have become aware of their actions in the contemporary urban landscape. This paper highlights possible design strategies that domesticate leftover spaces of diverse scales by injecting creative and playful programs, using Tokyo as a paradigmatic case study. More so than other global metropolises, the city represents a living laboratory for experimentation due to its compactness and the variety of urban patterns. Its leftover spaces demonstrate how play positively affects everyday life in public spaces, and how it enables extraordinary uses. A combination of ethnographic observations and spatial analysis is applied as a trans-disciplinary method. This approach allows an understanding of how people use playfulness to transform, appropriate, and utilize leftover spaces, which serves as guidance for urban planners and designers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".