Urban disaster recovery: a measurement framework and its application to the 1995 Kobe earthquake
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
This paper provides a framework for assessing empirical patterns of urban disaster recovery through the use of statistical indicators. Such a framework is needed to develop systematic knowledge on how cities recover from disasters. The proposed framework addresses such issues as defining recovery, filtering out exogenous influences unrelated to the disaster, and making comparisons across disparate areas or events. It is applied to document how Kobe City, Japan, recovered from the catastrophic 1995 earthquake. Findings indicate that while aggregate population regained pre-disaster levels in ten years, population had shifted away from the older urban core. Economic recovery was characterised by a three to four year temporary boost in reconstruction activities, followed by settlement at a level some ten per cent below pre-disaster levels. Other long-term effects included substantial losses of port activity and sectoral shifts toward services and large businesses. These patterns of change and disparity generally accelerated pre-disaster trends.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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