Law in Society: The Impact of Green Criminology on the Security and Stability of Society and the relevant legislation in China and Canada
Bibliographic record
Abstract
In response to rapid social development and substantial economic restructuring, there are further exacerbated by the heightened global focus on the Paris Agreement, the urgency of addressing global environmental challenges has significantly increased. Consequently, the field of green criminology has increasingly entered public discourse, reflecting a growing concern for environmental justice. This study aims to examine the effectiveness of legislation related to green criminology in Canada and China. It primarily analyzes the existing legal frameworks within these countries using a document analysis approach, supplemented by comparative and qualitative research methodologies. The findings indicate that Canada has developed a relatively sophisticated system of fines, while China places a greater emphasis on penal sanctions. Both countries have tailored their green criminology penalties to fit their specific social contexts and unique legal systems. However, a comparative analysis between the two still reveals that the penalties maintain significant flaws in both countries, suggesting an ongoing need for legal refinement and adaptation to more effectively address environmental crimes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".