The Distribution of Environmental Risks: Analytical Methods and French Data
Bibliographic record
Abstract
Résumé Les risques sanitaires liés à l’environnement commencent à être reconnus en France. Le ministère chargé de l’Écologie et du Développement durable estime que 30 000 décès prématurés par an et 7 % à 20 % des cancers seraient liés à des facteurs environnementaux, comme les pollutions de sources diffuses (transports, utilisation de pesticides) et localisées (incinérateurs, décharges, sites industriels). Des inégalités sociales en matière d’exposition aux risques environnementaux ont été observées dans de nombreux pays industrialisés (États-Unis, Canada, Royaume-Uni, Pays-Bas, Allemagne). La question se pose donc aussi pour la France : sommes-nous égaux devant la pollution ou bien les populations les plus démunies sont-elles aussi les plus exposées ? Cet article présente un état des savoirs méthodologiques et analytiques sur la distribution sociale des risques environnementaux afin d’alimenter la recherche sur ce sujet en France.
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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 | 0.039 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".