Comparative Study of Legislations on Major Domestic and Foreign Environmental Pollution Crimes
Bibliographic record
Abstract
Surveying across Germany, Japan, The United Kingdom, and the United States’ environmental pollution crime legislations, there are similarities and differences, and these similar or different models or regulations reflect their different environmental states, legal cultures, legal traditions, political systems, levels of economic development, etc., and have achieved positive results in their own countries. Having been inspired by these countries which are sophisticated in the trend of environmental protection and mature in environmental criminal legislation, our country should also discover a path that is suitable for us according to our own environmental pollution problems and practices. It is suggested that major environmental pollution crimes’ relevant regulations are to be further modified from the perspective of how things ought to be. Possible flaws in the legislative techniques aside, fundamentally speaking, a lot of the other problems or deficiencies stem from just what kind of value system major environmental pollution crimes are systematically constructed and responsibilities allocated. Only by coming from a correct and sound value system can there be an effective guidance to the scientific design of the regulations of these types of crimes and to have it be effective in practice when preventing and remedying major environmental pollutions.
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 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".