Experimenting with urban stressors: a network-based approach to systemic resilience through the URSA framework
Bibliographic record
Abstract
Understanding how shocks cascade through interdependent urban systems is essential for designing effective resilience strategies. We introduce the Urban Resilience and Sustainability Alliance (URSA), a network-based framework that represents a city’s physical, socioeconomic, and sociocultural dimensions as a multi-domain directed network of indicators, coupled with the URSA-RSPM (Realistic Stress Propagation Model). URSA integrates structural diagnostics with an adaptive contagion algorithm to quantify network resistance through defined metrics and to simulate both passive and policy-driven recovery. Computational experiments illustrate how URSA-RSPM identifies latent vulnerabilities and evaluates “what-if” interventions. In a Vancouver case study of a hypothetical tariff shock to the Public Finance and Business Environment indicators, the model reveals rapid cross-domain cascades when no action is taken and demonstrates that timely fiscal support can contain widespread failure. By serving as a city-scale digital living lab, URSA-RSPM enables planners and policy analysts to explore multi-hazard and compound-risk scenarios in a safe, data-driven environment, supporting iterative policy design and adaptive urban governance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".