Evaluating long-term urban resilience through an examination of the history of green spaces in Tokyo
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Long-term urban resilience requires urban systems with the capacity to respond to change and disturbance and to enhance the conditions for lasting wellbeing. Over the past century Tokyo has demonstrated impressive resilience, especially a capacity to reorganise and rebuild in response to successive major disturbances. Throughout these recoveries, the city-region maintained a focus on re-establishing, improving and maintaining international competitiveness through industrial development. Green spaces in Tokyo provided a flexible, but gradually disappearing resource. Today, to meet the needs of its ageing and minimally expanding population for enhanced wellbeing, Tokyo requires active transition planning covering many intertwined factors, but the adaptive capacity provided by the green space resource is no longer available. The Tokyo case underscores the risk inherent in the depletion of non-renewable resources (in this instance, green space) to secure immediate recovery and accommodate growth and short-term resilience at the expense of long-term resilience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it