Assessing the âHazards of Placeâ Model of Vulnerability: A Case Study of Waterloo Region
Bibliographic record
Abstract
This research project examines the Hazards of Place model of vulnerability (as developed by Cutter, 1996) to determine whether it is applicable in a Canadian context.\nAn in-depth case study of the Regional Municipality of Waterloo was used to determine whether the model accurately describes: emergency and community practitioners understandings of vulnerability and vulnerable populations in Waterloo Region emergency and community practitioners perceptions of the variables that influence vulnerabilities mitigation and preparedness efforts that could be enhanced and/or implemented to reduce the vulnerability of individuals and groups in Waterloo Region \nTo complete this study, in-depth interviews and surveys were conducted with a variety of emergency management practitioners and community organizations at the regional, as well lower-tier municipal levels. The results of the research indicate that the Hazards of Place model of vulnerability provides a reasonably accurate portrayal of emergency practitioners understanding of vulnerability, although some additional variables that influence vulnerability were introduced. Throughout this research, emphasis on building community and individual resilience was also promoted as a key factor in reducing the human and economic losses associated with disaster events. This led to an enhanced version of the ‘Hazards of Place’ model which recognized the layered and dynamic processes of vulnerability and resilience. Through this, a new understanding of the overall place resiliency was presented which merges the vulnerability and resilience literature to create a new understanding of the relationship between these two concepts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".