Governing urban resilience: Organisational structures and coordination strategies in 20 North American city governments
Bibliographic record
Abstract
This paper describes how urban resilience governance is structured and coordinated in 20 North American cities (19 US and one Canadian) based on interviews with city officials. This co-produced research evolved out of conversations with city officials in Portland, Oregon, who were interested to learn how other cities were organising resilience work. Interviews focused on emerging definitions, organisational structures, internal and external coordination efforts, and practitioners’ insights. The paper includes a descriptive summary of how cities are structuring and coordinating resilience efforts. Additionally, we discuss how current trends in resilience coordination can inform future directions for urban resilience scholarship. We compare what practitioners view as key success factors against six commonly theorised characteristics for effective resilience governance. Overall, we find considerable overlap in lessons from theory and practice, including the benefits of a systems approach, the need for a clear definition of resilience, strong leadership, and stakeholder engagement. Practitioners use resilience to diagnose the overall health of their cities. Additionally, practice tends to emphasise limitations such as political turnover, trade-offs between centralised and dispersed organisation, and the need to carefully diagnose and scope resilience work, whereas the academic literature calls for multi-level and cross-scale governance and feedbacks and more transformative action. Given these insights, we highlight opportunities for new resilience scholarship, including analysing the benefits of the diagnostic phase of resilience planning, evaluating resilience goals to determine the best departmental fit, and understanding local barriers and trade-offs to adopting a broad systems approach.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".