Urban resilience operationalization issues in climate risk management: A review
Bibliographic record
Abstract
This paper presents a review of existing strategies and tools aiming at facilitating the operationalization of the concept of resilience into built environments. In a context of climate change , increased risks in urban areas and growing uncertainties, urban managers are forced to innovate in order to design appropriate new risk management strategies. Among these strategies, making cities resilient has become an imperative. However, resilience remains complex to integrate into the practices of urban planners and territorial actors. Its multitude of definitions and approaches has contributed to its abstraction and lack of operationalization. This review highlights the multitude of approaches and methodologies to address the bias of the lack of integration of the concept of resilience in climate risk management. The limit is the multiplication of these strategies which lead to conceptual vagueness and a lack of tangible application at the level of local actors. The challenge would then be to design a toolbox to concentrate the various existing tools, conceptual models and decision support systems in order to facilitate the autonomy and responsibility of local stakeholders in integrating the concept of resilience into risk management strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".