Social equity in urban resilience planning
Bibliographic record
Abstract
A growing number of cities are incorporating resilience into their plans and policies to respond to shocks, stresses, and uncertainties. While some scholars advocate for the potential of resilience research and practice, others argue that it promotes an inherently conservative and neoliberal agenda, prevents systemic transformations, and pays insufficient attention to power, politics, and justice. Notably, critics of the urban resilience agenda argue that policies fail to adequately address social equity issues. This study seeks to inform these debates by providing a cross-sectional analysis of how issues of equity are incorporated into urban resilience planning. We develop a tripartite framework of equity that includes distributional, recognitional, and procedural dimensions and use it to analyse the goals, priorities, and strategies of formal resilience plans created by member cities of the Rockefeller Foundation’s 100 Resilient Cities programme. Our analysis reveals considerable variation in the extent to which cities focus on equity, implying that resilience may be more nuanced than some critics suggest. There are, however, clear areas for improvement. Dominant conceptions of equity are generally tied to a distributional orientation, with less focus on the recognitional and procedural dimensions. We hope our conceptual framework and lessons learned from this study can inform more just resilience planning and provide a foundation for future research on the equity implications of resilience.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".