Measuring, Monitoring, and Evaluating Post-Disaster Recovery: A Key Element in Understanding Community Resilience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it