Juvenile Offenders: Reasons and Characteristics of Criminal Behavior
Bibliographic record
Abstract
The article examines the phenomenon of “juvenile delinquency”, assesses its actual state and establishes the tendencies of its manifestations. Juvenile delinquency in Ukraine as a part of crime in a broad sense arises and develops under the influence of certain determinants. The study of the causes and conditions of juvenile delinquency remains relevant today, which indicates the special danger of this kind of crime for the development of society. The purpose of the article is to study the state of the problem in Ukraine and the experience of other countries in minimising the criminal behaviour of minors in the process of property and non-property relations. The leading approach that was used when writing the article is the comparison and analysis of modern materials on the problems of criminal behaviour of criminals who have not reached the age of majority. As a result, it was possible to identify the social characteristics of juvenile criminals and the reasons for their criminal behaviour. Considerable attention is paid to the factors influencing the commission of crimes: a dysfunctional family, shortcomings of the educational process, the problem of alcohol and drug use by minors. In addition, some directions for the prevention of juvenile delinquency were developed. The applied value is the ability to change legislation in terms of work and correction of minor criminals’ behaviour.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".