OBJECTIVE SIGNS OF ENVIRONMENTAL CRIMES AND THEIR FEATURES: ANALYSIS AND PROPOSALS
Bibliographic record
Abstract
In this article, such research methods were widely used as logical, systemic, comparative legal. In particular, the article explains in detail such concepts as in the sphere of environmental, general environmental crimes and special environmental crimes crime, gives the opinions of scientists about the signs of a crime, such signs as the social dangers of a crime, illegality, delinquency and inevitability of punishment. It also highlights the necessary signs of a crime, the opinions expressed by scientists in the theory of criminal law about these signs, and then the elements of the corpus delicti and the objective signs of the corpus delicti that characterize these elements are consistently described. This reflects the views and ideas of not only scholars of the Romano-Germanic legal family, but also scholars of the Anglo-Saxon legal family. In addition, the main attention in this article is paid to theoretical and practical problems related to the criminal-legal value of the subjective and objective signs of a crime and its specific criminal-legal aspects, as well as the necessary and optional signs of the corpus delicti of some crimes listed in the Criminal Code of the Republic of Uzbekistan. At the same time, the criminal legislation of the United States, Great Britain, Canada, Japan and the Russian Federation is analyzed, in connection with which specific proposals and recommendations have been developed for improving the criminal legislation of the Republic of Uzbekistan.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".