Application of Risk Perception Theory to Develop a Measurement Framework for City Resilience: Case Study of Suita, Japan
Bibliographic record
Abstract
Risk management has developed as an important aspect of sustainability. In order to manage risk more effectively, an overall evaluation of regional resilience needs to be performed. Therefore, this paper develops a framework to measure overall resilience in a community, focusing on risk perceptions of citizens of Suita City, Japan. The framework includes three main phases: (1) identifying multiple risks in the city through discussions with local experts and city workers; (2) prioritizing those risks by applying principal component analysis (PCA); and (3) understanding the relationships among them using decision-making trial and evaluation laboratory (DEMATEL) analysis. As a result, 21 risks were identified, and subsequently, four risks were prioritized: climate change, lack of self-sufficient energy, damage to the ecosystem, and natural disasters. Lastly, the application of DEMATEL analysis revealed that climate change and natural disasters have the greatest cause-effect relationships among the risks. The framework proves that multiple risks can be prioritized and gives overall suggestions on what kinds of risk a community is facing; where to start considering how to manage resilience; and which functions/services a community should improve to boost resilience. The identification, prioritization, and visualization of significant risk relationships completed in this study can support decision-making processes in strengthening community resilience.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".