An integrated approach for community hazard, impact, risk and vulnerability analysis : HIRV
Bibliographic record
Abstract
The Great Hyogo-Ken Nanbu earthquake, Hurricane Andrew, the Lockerbie air crash, and many other disasters have had terrible impacts on communities around the world. Disasters will continue to occur, and their social, economic, political, and environmental impacts will continue to increase. Communities are becoming increasingly concerned about this and are working to develop disaster management programs to prepare for, respond to, and recover from disasters. Hazard, risk, and vulnerability (HRV) analyses form the basis of disaster management processes; unfortunately, to this point, communities and regional districts have not had access to effective HRV models. This dissertation focuses on HRV analyses that are community-based, and it argues that the goal of such analyses should be to assist communities in developing and prioritizing mitigation strategies for hazard management. It also argues that HRV models should allow for the integration of disaster management and community planning, along with a high degree of public participation. Through a literature review, fourteen key objectives for determining the adequacy of current HRV models are derived. When extant models are measured against these objectives, it becomes clear that the former are deficient in a number of areas. In order to rectify these deficiencies, a new HRV model - the hazard, impact, risk, and vulnerability (HIRV) model is introduced. The HIRV model is developed through extensive use of exploratory studies and (1) incorporates a high degree of public participation, (2) is all-hazard in scope, (3) provides for realistic and practical risk assessment, (4) establishes guidelines for determining vulnerabilities, (5) provides guidelines for determining the potential impacts of a disaster, and (6) provides a method for prioritizing mitigation strategies. The potential effectiveness of the implementation of the HIRV model is evaluated through the use of participatory case studies in the British Columbia communities of Barriere, Taylor, and Kamloops. In short, the HIRV model provides a way for communities and emergency planners to make effective use of existing resources in order to develop comprehensive and practical disaster management programs and to move towards sustainable hazard mitigation.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".