The Application of Composite Indicators to Disaster Resilience: A Case Study in Osaka Prefecture, Japan
Bibliographic record
Abstract
This paper presents an empirical verification of the measurement of baseline characteristics for fostering regional resilience. A set of indicators was selected from previous studies of disaster resilience, and an environmental element was added. The aims of the study were (1) to select a set of indicators that could be used for measuring disaster resilience, based on a review of the research literature, (2) to evaluate these indicators using the statistical approach of standardization, and to visualize the results using Geographic Information System (GIS) technology, and (3) to identify the key resilience characteristics using principal component analysis (PCA). The study focused on 29 municipalities in Osaka Prefecture, Japan. From the literature review, a total of 17 disaster resilience indicators were selected, covering economic, social, and community connection factors. The novel environmental attributes were selected from the literature on environmental sustainability. The standardized measures demonstrated that municipalities with a high level of resilience were also ranked highly on both the “social” and “community connection” attributes. The GIS mapping resulted a prominent urban-suburban divide, with urban areas having a lower level of resilience than suburban areas. The PCA demonstrated significant variation across the 29 municipalities, characterized by the factors “living standard” and “regional involvement.” An understanding of these baseline characteristics would allow governments to monitor chronological changes in the resilience of specific regions. This information can be used to support the establishment of an evaluation platform, and can contribute to a more systematic management of resilience.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".