Progress in assessing resilience to climate-related flood risks thanks to a comparative study between two francophone methods
Bibliographic record
Abstract
Urban resilience has now become an international imperative. Preparing, adapting and assisting urban territories in a resilient adaptation to climate risks is one of the modern challenges. However, the concept of resilience remains fuzzy, complicated to operationalize for local actors. Operationalization requires first of all the measurement of resilience levels. This measurement is done above all through the development of indicators. The indicators are often based on the analysis of the resilience of critical infrastructure. Questions of material resistance and return to service are the most frequent. Measuring the territory and the population in a holistic and systemic way is more complex. However, some approaches have co-developed indicators with local actors to measure and define urban resilience by integrating socio-economic, urban, technical and environmental dimensions. We will analyze here two approaches: one developed and tested on two French territories, the other developed and tested in municipalities of Quebec.What advances have been made in the development and application of urban resilience indicators in Francophone territories? What types of data are used and what are the geovisualization tools? What are the best choices to develop decision support processes with local actors? This comparison between the different methods and respective advances will allow to identify the most suitable practices for initiating and consolidating informed decision support and innovative practices in terms of urban 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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".