Conceptualizing Dimensions and Characteristics of Urban Resilience: Insights from a Co-Design Process
Bibliographic record
Abstract
Resilience is a multi-faceted concept frequently used across a wide range of disciplines, practices, and sectors. There is a growing recognition of the utility of resilience as a bridging concept that can facilitate inter-and transdisciplinary approaches to tackle complexities inherent in decision making under conditions of risk and uncertainty. Such conditions are common in urban planning, infrastructure planning, asset management, emergency planning, crisis management, and development processes where systemic interdependencies and interests at stake influence decisions and outcomes. A major challenge that can undermine the use of resilience for guiding planning activities is the value-laden and contested nature of the concept that can be interpreted in a variety of ways. Because resilience is context-specific and generally depends on local aspirations, this issue can be partially tackled by adopting participatory approaches for the conceptualization of resilience. This paper provides an example of how co-design methods can be employed for conceptualizing resilience. The Structured Interview Matrix was used as a technique to facilitate discussions among a diverse group of researchers and practitioners attending the International Workshop on Tools and Indicators for Assessing Urban Resilience. Participants deliberated on issues related to constituent elements of urban resilience, including its position vis-
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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 | 0.065 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| 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".