Comment analyser l’absence d’anticipation des risques? Le cas de la canicule de 2003 en France
Bibliographic record
Abstract
La canicule qui a eu lieu en France en 2003 reste dans les mémoires comme un cas exemplaire d’absence d’anticipation des risques sanitaires qui s’est soldée par une surmortalité de 15 000 personnes. Comment expliquer cette défaillance avec les concepts des sciences sociales? L’approche sociologique des catastrophes climatiques, telle qu’elle a été illustrée par Klinenberg (2002), permet de dénaturaliser l’événement et de montrer comment il est socialement construit. Nous proposons de compléter cette perspective par celle de la sociologie des organisations pour comprendre pourquoi les systèmes d’alertes n’ont pas permis de détecter les risques sanitaires. À partir d’une analyse secondaire des rapports officiels et de travaux de recherche, nous formons une typologie des obstacles à la production et à la circulation des informations parmi les acteurs et les institutions chargés de protéger la santé du public.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".