Measuring, Monitoring, and Evaluating Post-Disaster Recovery: A Key Element in Understanding Community Resilience
Bibliographic record
Abstract
The process of community recovery in the aftermath of a disaster is complex, long lasting, resource intensive, and poorly understood. Insights described here result from an ongoing project that aims to monitor, quantify, and evaluate the process of post-disaster recovery for two events, Hurricane Charley (2004, Charlotte County and Punta Gorda, Florida) and Hurricane Katrina (2005, Harrison County and Biloxi, Mississippi). A mixed-methods approach using statistical data, interviews, and remote sensing-derived data is applied in an effort to understand as well as monitor, measure and evaluate the recovery process and its outcomes. Observations associated with the post-disaster course of moving residents from temporary to transitional, and ultimately permanent housing serves as the focus for this paper. This work represents a discrete portion of a multi-sector project where Economic, Environmental, Housing/Infrastructure, and Social elements of community recovery are explored. Understanding community recovery can inform community resilience-building strategies.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".