Socio-legal Problems of Environment Criminal-legal Protection, Regarding Illegal Deforestation
Bibliographic record
Abstract
Environment protection from illegal encroachments and prevention of ecological criminal illegality are among the main tasks of the Law of Ukraine on Criminal Liability.The criminal law must ensure the constitutional right of every person from adverse environmental encroachments and guarantee the constitutional protection of natural resources as the basis of human life and activity.Forest ia a environment component has a huge resource potential and performs a variety of environmental, economic, cultural and recreational functions.Recently, a criminal offense in the field of illegal deforestation has become very common in our country (Article 246 of the Criminal Codex of Ukraine).The article analyzes the most common factors and conditions for the growing number of criminal offenses related to illegal deforestation.Ukraine is on the path to devastation.The article analyzes the state of forest plantations change over the past 10 years and trends towards further destruction of the country's forest fund.The article is based not only on the register of court decisions, but also on a questionnaire conducted among forestry workers and police officers involved in the pre-trial investigation of criminal offenses related to illegal deforestation.The legislative materials regarding to the regulation of ecological protection in the field of illegal deforestation are analyzed.The most common criminal offenses concealment schemes are considered, the main criminological factors influencing the spread of criminal offenses related to forest protection are identified.Measures to be taken to stop the spread of these socially dangerous acts against the environment are proposed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".