Lessons Learned in Dealing with Large-Scale Disasters
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
Many OECD countries have been affected by major harmful events in recent years. The considerable human and economic costs of such events and the repercussion they might have for the global economy have become recurring causes for concern. Given its intergovernmental and multidisciplinary nature, and its experience in risk and disaster management in a variety of fields, the OECD is well positioned to analyse the impact of major disasters on societies and economies, and to identify optimal practices in response and recovery phases. To this end, the OECD’s International Futures Programme supervised a team of specialists from eight OECD directorates, and a team of Turkish specialists who provided the material for chapter 3. The report was prepared between May and July 2003. This report analyses the economic and social impacts of recent large-scale disasters, and draws some initial lessons for the monitoring and the management of future disasters. The report primarily focuses on restoring trust and securing recovery after a major harmful event has occurred. The events reviewed are as diverse as the Chernobyl nuclear accident, the Kobe and Marmara earthquakes, Hurricane Andrew, and the 11 th September terrorist attacks on New York and Washington. Disasters such as these have in common massive effects on large concentrations of people, activity and wealth. They disrupt multiple vital Systems such as energy supplies, transport and communications. Their effects spread beyond the region originally affected and generate widespread anxiety. In some cases, the public expresses distrust of the ability of governments to protect citizens.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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