L'influence des systèmes d'indemnisation dans les choix résidentiels en contexte de risques côtiers en France et au Québec
Bibliographic record
Abstract
Despite increasing coastal erosion and flooding in the context of climate change, the appeal of living by the sea remains strong in both France and Quebec. Understanding what drives individuals to purchase or remain close to the shore is becoming a key issue for public authorities seeking to counter this trend and promote adaptation policies such as relocation. As part of a Franco-Quebec research project (ARICO), this jointly supervised doctoral thesis investigates a factor still largely overlooked in geography: the influence of compensation systems on coastal residential choices. In both France and Quebec, part of the costs associated with coastal risks are mutualized through solidarity-based compensation systems in which the state plays a central role. The hypothesis is that these systems may encourage individuals to purchase or remain by the sea, by shifting the financial burden of risk from individuals to the broader community.To examine this hypothesis, a multi-scale survey was conducted in France and Quebec, combining interviews with 54 private- and public-sector stakeholders and questionnaires completed by 371 coastal homeowners. The cross-analysis of interview and questionnaire data, and the France-Quebec comparison, reveal that these systems influence residential choices in different ways, depending on how solidarity is balanced with individual responsibility. This finding argues not for the abandonment of solidarity, but rather for a redefinition of how it is implemented.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| 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".