Domestic Violence and Crimes Against Minors: Challenges and Prospects for Reforming Criminal Legislation
Bibliographic record
Abstract
The article is devoted to a comprehensive study of crimes that infringe on the institutions of the family and minors, in the context of modern Russian legislation and taking into account foreign experience. The author analyzes in detail the legal nature of these acts, their social danger, as well as the system of punishments provided for their commission. The study reveals doctrinal approaches to defining the concept of “crimes against the family and minors”, identifies their key features, such as formal certainty and intentional nature. Specific types of crimes provided for in Articles 150-157 of Chapter 20 of the Criminal Code of the Russian Federation are considered, including the involvement of a minor in criminal activity, failure to fulfill parental responsibilities, substitution of a child, illegal adoption and others. A comparative legal analysis of Russian legislation with the norms in force in Lithuania, Japan, Israel, Canada and Sweden is carried out in terms of combating domestic violence. The advantages and disadvantages of various approaches to solving this problem are revealed. Particular attention is paid to the issues of guilt, intent and negligence in committing crimes against the family and minors. The author also touches upon the problem of the influence of legal culture and legal consciousness of Russian society on the level of crime in this area, noting its low level.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".