Measuring Improvements in the Disaster Resilience of Communities
Bibliographic record
Abstract
This paper demonstrates the concept of disaster resilience through the development and application of quantitative measures. As the idea of building disaster‐resilient communities gains acceptance, new methods are needed that go beyond estimating monetary losses and that address the complex, multiple dimensions of resilience. These dimensions include technical, organizational, social, and economic facets. This paper first proposes resilience measures that relate expected losses in future disasters to a community's seismic performance objectives. It then demonstrates these measures in a case study of the Memphis, Tennessee, water delivery system. An existing earthquake loss estimation model provides a starting point for quantifying resilience. The analysis compares two seismic retrofit strategies and finds that only one improves community resilience over the status quo. However, it does not raise resilience to an adequate degree. The exercise demonstrates that the resilience framework can be valuable for guiding mitigation and preparedness efforts. However, to fully implement the concept, new research on resilience is needed that goes beyond loss estimation modeling.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".