Equity and Resilience: Can Cities of the Future Achieve Both?
Bibliographic record
Abstract
Within the concept of city resilience lies an opportunity to transform current systems of power and oppression that perpetuate social inequities and deny basic human rights to much of the world’s population. This research examines how current resilience practices, if left unchecked, might affect the future equity of a city’s neighbourhoods and communities by fortifying oppressive power structures and systems dominant in today’s society. It questions how we might use systems thinking and foresight tools to re-engineer processes for building resilience that supports the transition to more equitable and just cities. A design research methodology was used to explore 1) what makes a future equitable; 2) the process by which we define a term, in this case, resilience; and 3) how this definition might hold power to inform how resilience is built, distributed, and regulated in the future. The methodology consists of field observation and semi-structured subject matter expert interviews while employing foresight methods, systems analysis, and generative design research techniques to facilitate multi-stakeholder engagements. Contributions of this research include recommendations on how we might re-engineer foundational processes for building definitions of resilience that consider equity and support the building and repairing of a just city. Additionally, this study introduces a conceptual tool, Dream Capital, for adapting and designing more equitable approaches to building resilience that can aid cities in overcoming social, political, economic, and cultural inequities in the future.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".