Foreign experience of investigating crimes committed by members of youth informal groups (associations)
Bibliographic record
Abstract
The article deals with specific issues of foreign experience in investigating crimes committed by members of informal youth groups (associations). It is emphasized that the study of foreign experience in the investigation of crimes committed by members of youth informal groups (associations) will contribute to the development and improvement of recommendations for the investigation of the corresponding category of criminal offenses in Ukraine.
 The peculiarities of detection and investigation of crimes committed by members of youth informal groups (associations) in France, Germany, the Netherlands, Canada and the USA are analyzed.
 Most often, French youth can unite in informal groups in connection with a common place of residence (street, district, city), because of race, nationality, political views, social crises, etc. Difficulties in investigating the criminal activities of informal youth in France are associated with the active use of social networks for the preparation and concealment of committed offenses, the organization of gangs, an increase in their number, and a great public outcry.
 All youth informal groups (associations) in Germany can be divided into 3 types: 1) associations of young people who are migrants, refugees and spend their free time together, live on the same street, in the same district; 2) neo-Nazi youth associations; 3) unification of youth on the basis of political views and protest sentiments.
 The boom in youth crime in the Netherlands occurred in the early 1980s. But a balanced preventive work, starting in 1985, gave results. An active struggle is being waged against youth neo-Nazi groups (associations) on the Internet.
 There are 434 youth gangs in Canada with approximately 7,070 members. The greatest concentration of youth gangs in British Columbia, Saskatchewan, Manitoba and Ontario.
 In the 50s. XX century. American youth, and students in particular, showed themselves for the first time as an active political force, able to defend their positions on an equal footing with political parties and «adult» public organizations. The youth of that time advocated civil liberties – freedom of speech, organizations, meetings, etc. But informal youth groups in the USA are not only a subcultural phenomenon, but also gangs that actively engage in criminal activities based on the promotion of ideas, views, and lifestyles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".