Inquiry Into the Subjectivity of Major Environmental Pollution Crimes: From the Perspective of Weak Anthropocentrism
Bibliographic record
Abstract
Now, scholars have done some research on the subjectivity of major environmental pollution crimes, and have raised some different points. It can be said that different people have different points. This paper views from the perspective of weak anthropocentrism, starts with the organization and introduction of the key theories on the subjectivity of contemporary major environmental pollution crimes and the subjective attitude of the major environmental pollution crimes in practice, and then delves into a comparative study of a variety of key theories on the subjectivity of crimes of major environmental pollution, emphasizes the distinction between the intentional and negligent nature of the subjectivity of crimes of major environmental pollution and the problem of strict liability of the subjectivity of crimes of major environmental pollution. Through research, it is argued that it is better to punish and prevent crimes of major environmental pollution if crimes of major environmental pollution incidents are separated, according to subjectivity, into crimes of intentional environmental pollution and crimes of negligent environmental pollution incidents, and the liability principle of crimes of major negligent environmental pollution incidents should be based on the relative strict liability.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".