Crimes environnementaux : si la pollution de l’eau tue… malheureusement elle rapporte !
Bibliographic record
Abstract
Qualifier la pollution de l’eau de crime peut sembler à première vue relever du sens commun et une telle pollution est déjà considérée comme un crime dans certains pays, à certaines conditions. Il importe toutefois de se demander ce qu’apporterait concrètement, pour lutter contre la dégradation et la pollution des ressources en eau, vitales, une telle criminalisation à l’échelle internationale. Du point de vue du message, la catégorie de crimes environnementaux aurait certes l’avantage de signifier la gravité des conséquences pour la société, et pour l’humanité. Dans ce domaine, comme pour les violations massives des droits humains, une telle criminalisation pourrait également contribuer à faire reculer, sinon l’impunité, du moins le sentiment d’impunité. Il demeure toutefois que la sanction effective de tels crimes requiert des conditions précises, souvent difficiles à satisfaire ; ainsi en est-il du fardeau de la preuve d’une part, et de la volonté politique d’autre part.
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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 | 0.001 | 0.006 |
| 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 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".