The Increasing Cost of Disasters in Developed Countries: A Challenge to Local Planning and Government
Bibliographic record
Abstract
The number and severity of disasters have increased in recent decades. Developed countries are not immune from this trend. Their governments and insurance industries are now being required to cope with rapidly increasing and unanticipated disaster expenditures. In some cases, disaster related claims have increased by more than a full order of magnitude in just a decade. It is important for local planners and governments to understand the general trend of disaster impacts in order to respond to them. To illustrate these trends, the increase in number and financial impact of the last decade of disasters in Canada are reviewed in this article along with some discussion of the impact on government and the insurance industry. In spite of the increasing impact of emergencies and disasters, Canadian local municipal governments have, in general, invested very little in emergency mitigation planning. Many municipalities have no emergency plans at all. Where plans exist, many only address a small range of possible threats, and many do not include any mitigation aspects. Emergency mitigation planning at the local municipal level is critical for effective mitigation and response. We review the general institutional context for local disaster and emergency planning in Canada, concluding that planners have sufficient tools available to begin to address the challenge. Political will and professional interest now is required to make the necessary advances.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".