Punishment for the Crimes against Person According the Criminal Code of Poland
Bibliographic record
Abstract
This study examines the types of sanctions for crimes against the person provided for by Special part of the Criminal code of Poland (hereinafter referred to as the Criminal code). The system is analyzed and in more detail-certain types of criminal attacks on the person in comparison with the analogous norms of the criminal code of the Russian Federation and the criminal legislation of some other States. The introduction substantiates the relevance of the research, defines the object and purpose of the research. Based on the research, a number of conclusions and recommendations for changing section VII of the Special part of the criminal code of the Russian Federation are formulated. Asked to borrow a positive experience of the Polish legislator to change the order of sections in a Special part of the Criminal code of the Russian Federation, justifies measures to prevent attacks on the person in connection with the pandemic coronavirus in addition the criminal code of Poland and the criminal code – establish liability for evading treatment of a difficult-to-treat infectious disease that is dangerous to others, as well as adopt a Law on the prevention of infection with a difficult-to-treat infectious disease, in which it would be possible to establish compulsory hospitalization, based on a court decision, to appropriate medical institutions for patients with these diseases who evade examination or treatment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".