Features of Criminal Liability of Juvenile Criminals: International Legal and Comparative Analysis
Bibliographic record
Abstract
The relevance of the problem under study lies in the fact that the criminal liability of juvenile criminals is one of the most difficult areas of criminal law. Minors, given their physiological, mental, and social characteristics, are considered a separate category of criminals, being one of the most vulnerable segments of the population. Therefore, juvenile delinquency manifests itself not only in causing harm to public relations, the personality of the victim, but also directly to the minor, forming antisocial behaviour in the latter's mind. Considering the above, the problem of the specific features of the criminal liability of minors remains relevant today. The purpose of the study is to analyse the criminal liability of minors from an international legal standpoint, as well as to carry out a comparative analysis of the features of the regulation of criminal liability of minors in different countries of the world. To fully explore the subject matter of the study, a set of general scientific and special methods of cognition was used. In particular, the study used the methods of scientific knowledge, system analysis, scientific abstraction, generalisation, comparison, analysis and synthesis, grouping, formalisation, historical and logical analysis. For example, the leading method was the comparison method, which helped to compare the specific features of practice in other countries of the world in criminal liability of minors. The study analyses the features of the criminal liability of minors, in particular the minimum age of criminal liability, differences between countries in this regard, as well as general international standards.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".