L’émergence et l’avenir de la criminologie environnementale1
Bibliographic record
Abstract
Cet article propose une introduction générale à la criminologie environnementale et à l’étude du crime environnemental. Il examine l’émergence de cette approche, les concepts clés, les questions empiriques et les enjeux susceptibles d’être abordés dans les futurs travaux dans ce domaine. L’article s’intéresse entre autres à l’écojustice (relative à des types spécifiques de victimisation environnementale) et à l’écocide (liée à la criminalisation du dommage environnemental). Les transgressions touchant les humains, les écosystèmes et les espèces non humaines offrent un cadre général de discussion des types spécifiques de crimes et de dommages, de la pêche illégale à la pollution contribuant au réchauffement climatique. Une analyse rétrospective et un tour d’horizon permettent de tracer les grandes lignes conceptuelles, passées et présentes, de la criminologie environnementale.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| 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".