The Production of Unequal Vulnerability to Flood Hazards in Metro Vancouver, Canada
Bibliographic record
Abstract
Flood risk is a growing concern in Canada’s cities. Residents of these cities have differential risk according to their unique vulnerability and exposure to flood hazards. Factors related to societal structural forces, human agency, and place interact to produce vulnerability to hazards. Analysis of the factors that influence vulnerability will lead to a better understanding of how unequal vulnerability to hazards is produced among residents of a city. This dissertation investigates the factors that influence vulnerability to flood hazards in a Canadian coastal urban region, Metro Vancouver. It develops and applies a conceptual framework for looking across scales and across actors to identify and situate the factors that influence vulnerability. The study uses multiple research methods to investigate these factors, including statistical analysis of population data, focus groups with municipal practitioners, a practitioner survey, a residential survey, and informal interviews with residents. The investigation centres on what I refer to as indicators and determinants of vulnerability, and how they interact to produce vulnerability. Social vulnerability is identified as an important determinant of vulnerability, and indicators of social vulnerability are tested with an index of population data at the neighbourhood scale. A participatory process illustrates that the input of municipal flood managers can be used to make a quantitative assessment of social vulnerability more meaningful to those working in local policy and reveals findings about how practitioners view vulnerability in their community. Institutional arrangements such as development regulations and property insurance are found to be another key determinant that influences vulnerability. The availability of overland flood insurance, as an institutional arrangement were it to be introduced in Canada, would have implications for residential vulnerability and the current regime of public flood management. Institutional arrangements appear to interact with social vulnerability and other determinants to allow some groups of people to live in hazardous places without taking on the full associated risk. The findings of the study offer insights to what produces vulnerability and how, or whether, policy measures can address these factors to equitably reduce risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".