Implementing Urban Resilience at the Local Level: Three Francophone Urban Case Studies
Bibliographic record
Abstract
Preparing for climate change and uncertainties associated with it has become the priority of urban territories. Among these climate risks, flood risk is the most expensive risk worldwide. Yet, local actors are struggling to operationalize the concept of resilience and to equip themselves to prepare and adapt their territories.Developing decision-making tools to support, guide and accompany territories in their adaptation is becoming an urgent and essential issue. Two decision support tools have been developed in France and in Quebec to increase the resilience of three territories at risk of river and coastal flooding. Located in the southwestern part of the province of Quebec, in the Laurentian region, and at the confluence of the North, Ouataouais and Saint-André rivers, the municipality of Saint-André -d'Argenteuil is particularly at risk. Faced with the severe flooding of 2017, the community has therefore embarked on a long-term resilience and adaptation process. The city of Avignon, in the south of France, is located at the confluence of the Rhône and Durance rivers. The numerous floods that the city has experienced has allowed the development of a risk culture, enabling the implementation of resilience strategies with local stakeholders. Finally, French Polynesia is extremely vulnerable to climate risks. The major floods of 2017, which paralyzed the territory, alerted and trained local actors in a search for adapted and long-term resilience strategies.We will analyze the issues of these territories, the two tools co-developed with local actors, their respective contributions and their limits. Finally, we will question the potential genericity of the tools and their capacity to support different types of territories and local actors over the long term.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".