Disaster Risk Analysis Part 1: The Importance of Including Rare Events
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.
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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 | 0.032 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".