Applying a framework for defining emergency management scenarios
Bibliographic record
Abstract
Introduction: Scenarios are used extensively to support emergency management (EM). Virtually every user within the community, from policymakers to first responders, uses scenarios in one guise or another. They provide the context to characterize a dynamic problem space, to support the rehearsal of response options, and to facilitate the evaluation of new technology. With such far-reaching implications, there needs to be a means to guide scenario selection.Objective: The Canadian Centre for Security Science sponsored the development of a framework to characterize scenarios and to assist in evaluating EM capabilities, explicitly in the area of chemical, biological, radiological, and nuclear response. The framework also complements capability-based planning and provides a means to share scenarios.Methodology: The Public Safety and Security Planning Scenario Framework assists the EM community, which ranges from the national to the community level, by selecting scenarios based on user perspectives and objectives. In developing the framework, three challenges were addressed: a taxonomy was required to frame and define what constitutes a scenario; parameters were needed to describe and characterize scenarios; and structure was called for to assist in ordering the collection and comparison of representative scenarios.The first challenge involved reviewing existing literature to define the term “scenario.” Typically, scenarios are used to consider near-term threats, to capture planning assumptions, and to provide the perspective necessary to assess concepts and capabilities. The framework proposes a set of criteria or dimensions (eg, risks, triggers, and time horizons) that can be used to characterize scenarios. To test the framework, a representative set of scenarios was cataloged using these dimensions. Analysis of the resulting set was instructive in revealing the differences in planning scenarios across the chemical, biological, radiological/nuclear, and explosive communities. As the framework matures, it is hoped that it will promote information reuse and provide a valuable forum for capturing best practices and developing standards, enhancing efficiency and effectiveness improvements both locally and nationally.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".