An Integrated Measurement Framework of City Resilience for Preparedness: A Case Study for Japan
Bibliographic record
Abstract
In order to increase the resilience of cities, there has been substantial effort to improve preparedness for, and response to, unexpected disasters. However, there is no specific measurement framework to address the degree of preparedness of a city. This study proposes the development of such a framework, in three phases: (1) identify multiple risks to a city, using risk perception theory, (2) evaluate and categorize these risks, according to public risk perception, using principal components analysis (PCA), and, (3) following the selection of risks, evaluate the resilience policy structure by counting the number of existing policies and using analytic hierarchy process (AHP). This study was customized for eight representative cities in Japan. Twenty-eight risks were identified and categorized as “Risk anxiety level” and “Preventive controllability”, based on public risk perception. Following the selection of four risks – greenhouse gas generation, energy shortage, ecological destruction, and earthquake – the policy evaluation indicated that earthquakes have the strongest resilience policy structure in all eight cities. This was also reflected in the degree of city preparedness for resilience, which suggested that every city has relatively higher preparedness for earthquakes among the risks. These findings suggest that these cities’ policies are well engaged with public concern. The study provides information that can help policy makers to improve communication with the public to meet well-intentioned policy, to predict public response to potential risks, and to direct educational efforts. Such information can also be helpful in redefining policy approaches to strengthen cities’ and residents’ preparedness for external stresses.
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How this classification was reachedexpand
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".