Social Resilience to Flooding in Vancouver: The Issue of Scale
Bibliographic record
Abstract
Socioeconomic characteristics are commonly used as indicators of vulnerability and resilience to inform disaster risk planning and management. Vancouver is a coastal seaport city along the west coast of Canada and is exposed to risk from the impacts of flooding. Previous studies have assessed and modelled a city's resilience to environmental hazards based on socioeconomic status derived from census data, such as income status, family structure, and dwelling conditions. However, these data sources are aggregated into different census units of varying scale, such as Census Tracts (CT) and Dissemination Areas (DA). Spatial analysis of the same data using different aggregation units manifests in the Modifiable Areal Unit Problem (MAUP), where varying scale can produce different results and conclusions. This exploratory analysis of the MAUP demonstrates that social resilience to flooding hazards in Vancouver at the CT and DA census scales can have contradictory results depending on the census scale adopted. The effect of scale and the aggregation units at which spatial analysis occurs can have a significant impact on the conclusions imparted and decision making to identify priority areas. Since individual-level disaggregate data is unavailable, the analytical results based solely on aggregate data should be interpreted with caution.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".