Disaster Risk Analysis Part 1: The Importance of Including Rare Events
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
Abstract Rare events or worst-case scenarios are often excluded from disaster risk analysis. Their inclusion can be very challenging, both from methodological and data availability perspectives. We argue that despite these challenges, not including worst-case scenarios in disaster risk analysis seriously underestimates total risk. It is well known that disaster data sets generally have fat tails. In this paper we analyze data for a number of disaster types in order to empirically examine the relative importance of the few most damaging events. The data show consistent fat-tail trends, which suggests that rare events are important to include in a disaster risk analysis given their percentage contributions to cumulative damage. An example of biased risk estimation is demonstrated by a case study of risk analysis of tanker spills off the western coast of Canada. Incorporating worst-case scenarios into disaster risk analysis both reduces the likelihood of developing fantasy planning documents, and has numerous benefits as evidenced by applications of foresight analysis in the public sector. A separate paper "Disaster Risk Analysis Part 2" explores how disaster risk analyses are operationalized in governmental emergency management organizations, and finds evidence of a systemic underestimation of risk.
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 | 0.002 | 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 | 0.000 | 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) | 0.001 | 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 it