Social equity in urban resilience planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it