Un regard psycho-socio-environnemental sur les risques côtiers : une étude de cas en France et au Canada
Bibliographic record
Abstract
Over the last few decades, the occupation of coastal areas has intensified throughout the world (Meur-Ferec & Morel, 2004). These areas have become increasingly artificial, whether due to the development of tourist infrastructures or the multiplication of second homes. In a context marked by climate change, manifested among other things by a rise in sea level, these areas are subject to new challenges in terms of adaptation (IPCC, 2019).The issue of coastal risks (submersion and marine erosion) is a pressing one and concerns different sectors of society, whether it be the economic sphere (e.g. maintaining the tourist offer), the political world (e.g. managing urbanization and risks) or civil society (e.g. the users and inhabitants of these areas).Our research focuses on the social representations of these risks, mobilized by the individuals who live and/or work in these coastal territories. It was conducted in an international context, in several territories at risk in France and Canada. Different survey techniques were used: interviews, questionnaires, word association tasks, and content analysis of press articles. Our results show, contrary to the dominant scientific considerations which claim to give an objective and realistic evaluation of the dangers, the existence of a social construction of the risk dependent on an environmental and sociocultural context.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".