Federal emergency management in Canada and the United States after 11 September 2001
Bibliographic record
Abstract
Abstract: Research in the field of emergency management indicates that pre‐disaster mitigation can significantly reduce the costs of post‐disaster reconstruction and recovery. Yet, it is often difficult for disaster mitigation advocates to garner the support of policy‐ and decision‐makers, who tend to focus on other community concerns. Interest in disaster mitigation tends to be highest during the period immediately following a major disaster, when public attention focuses on vulnerabilities that must be addressed through policy. The “focusing event” of 11 September 2001 highlighted the vulnerability of a large urban area to disaster, in this case human‐induced. The event had a significant impact on federal emergency management in Canada and the United States and shaped the nature of mitigation policy in the following year. Sommaire: La recherche dans le domaine de la gestion des mesures d'urgence indique que les plans de protection contre les catastrophes peuvent grandement réduire les coûts de reconstruction et de rétablissement entraînés par me catastrophe. Cependant, il est souvent difficile pour les défenseurs de l'atténuation des effets de catastrophes d'obtenir le soutien des décideurs et responsables de l'élaboration de politiques qui ont tendance à se concentrer sur d'autres préoccupations communautaires. L'intérét portéà l'atténuation des effets de catastrophes a tendance àêtre à son paroxysme immédiatement après une catastrophe majeure, lorsque l'attention du public est fixée sur les vulnérabilités qu'il faut traiter par le biais d'une politique. L'événement marquant des attentats du 11 septembre 2001 a montré la vul‐nérabilité d'une vaste zone urbaine à une catastrophe, causée dans ce cas par l'homme. Cet événement a eu un impact important sur la gestion des mesures d'urgence au Canada et aux États‐Unis et a déterminé la nature de la politique d'atténuation des effets de catastrophes pendant l'année qui a suivi.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".