Planning for Long‐Term Recovery Before Disaster Strikes: Case Studies of 4 US Cities: A Final Project Report
Bibliographic record
Abstract
Among the four phases along the hazard continuum -- preparedness, response, recovery, and mitigation -- the sub‐field of long‐term recovery has long been an outlier, an "orphan" when it comes to concerted policy attention and pre‐disaster planning. It's not that community residents or municipal and state government officials are unaware of the potential long‐term residual consequences of natural disasters. Since the attacks of September 11, 2001 and the subsequent creation of the Department of Homeland Security, the U.S. government has spent billions of dollars to upgrade and enhance the country's ability to detect and respond to major catastrophic events, whether man‐made or natural in origin. The country experienced catastrophic wildfires in 2003, 2007‐2008, and 2011, a regional electrical blackout affecting 9 states and part of Canada in 2003, major Midwest flooding in 2008 and again this year, Category 3 or greater hurricanes in 2004, 2005, and 2008, and significant tornado clusters in 2011 that claimed 529 lives and caused over $17 billion in damages. These hazards have struck virtually every region of the country, and the consequences are readily evident to emergency managers and local city and county. Although the ratio of uncovered to covered losses has declined over this three‐decade timeframe, from approximately 8:1 to 4:1, absolute dollar losses have escalated tremendously. This may represent gains in mitigation efforts to insure against losses in high‐risk areas, but the size and growth of uncovered losses suggest a growing recovery challenge. This difference between covered and uncovered losses reflects the absolute minimum investment required for affected areas to return to pre‐event conditions, much less build back to a better or higher standard. Furthermore, what this trend line cannot capture are those disaster consequences not so easily monetized -- diminished physical and mental health among an affected citizenry, loss of a sense of community and attachment to place, or large scale social disruptions or population displacements. Given the magnitude of the social investment needed to pursue long‐term recovery after a disaster, and the attention that other phases in the hazard continuum have experienced, why is recovery still a policy orphan, and what are the local implications for pre‐disaster planning for long‐term recovery?
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".