Enabling strategies and impeding factors to urban resilience implementation: A scoping review
Bibliographic record
Abstract
Abstract Despite growing interest in urban resilience, there is a significant gap between discourse and the capacity to develop resilience in practice. This scoping review assembles and shares evidence and insights from empirical studies of attempts to implement urban resilience published between 2005 and 2017. More precisely, it seeks to identify enabling strategies, impeding factors and trade‐offs in the implementation of urban resilience. Findings are presented along the dimensions of urban resilience detailed in the City Resilience Framework (ARUP/Rockefeller Foundation): Health and Wellbeing, Economy and Society, Infrastructure and Environment, and Leadership and Strategy (which we present as a cross‐cutting theme). While some enabling and impeding factors in implementation are associated with a specific dimension, others are common to all three. Across dimensions, we find that transparent, inclusive and supportive governance reduces the risk of negative impact that resilience implementation will have on communities. Conflicting priorities of managing risk and meeting short‐term needs are found to diminish the potential for transformative resilience action. Integrating risk into planning appears as a promising strategy in all dimensions of resilience. Trade‐offs are found in resilience implementation, and range from adverse effects associated with infrastructure to power imbalances when the power to implement resilience privileges one system level over another.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".