Tokyo liminal spaces as a dispersed constellation of spatial identities
Bibliographic record
Abstract
In a metropolis and metropolitan public space, increased attention has recently been given to overlooked and uncontrolled spaces. Considered as spatial 'voids,' 'idle spaces,' 'interstices,' and 'in-between' spaces, they all have one characteristic in common: 'the waiting for use' potential that can be ignited by users' creativity and tenacity, and with designers taking the role of 'enablers' rather than 'deciders'. Hence, urban leftover space becomes meaningful place with a strong local identity, enabling new connections and maximising its socio-spatial potential. This paper analyses Tokyo as a paradigmatic case study to investigate the roles of local spatial practices in the process of leftovers' identity (re)construction. More so than other global metropolises, the city represents a living laboratory for experimentation due to its compactness and the variety of small-scale urban patterns. A combination of ethnographic observations and visual analysis is applied as a trans-disciplinary method to investigate small-scale urban leftovers in Tokyo's traditional urban tissue of the shitamachi districts. This approach allows an understanding of how individuals transform and utilise leftovers, which become a dispersed constellation of tangible spaces of identity. Extrapolation of home into a public zone of liminal leftover space, through appropriation and care, becomes the key to the resilience of local identities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".