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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.021 |
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; both teacher heads agree on what is shown here.
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".