Disaster Management: A State-of-the-Art Review
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
Disaster management involves the pillars of emergency management: planning and preparation, mitigation, response, and recovery. Emergencies are serious events that threaten health, life, and property and can be managed within the capabilities of the affected organization. Disasters, on the other hand, are hypercomplex emergencies, requiring resources not immediately available. Disaster management follows the principles of emergency management, and emphasizes flexibility, collaboration, and teamwork. Lack of resources will challenge people and organizations both in effects of disasters and the ability to manage them. Poverty, climate change, governance, and education are foundations to improve capacity. Hospitals play an important role in disaster response and can prepare accordingly. Plans, to be effective, must be implemented through appropriately-targeted exercises. Building on an all-hazards approach, to more hazard-specific considerations can improve disaster preparedness as well as day-to-day efficiency. Disaster management is complex and crucial. These principles are explored through the fictional tale of Tucci1, a coastal city in the worst flood anyone can remember. Well, almost anyone…
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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