Қазақстан Республикасында вандализм үшін жауаптылықтың құқықтық реттелуі және алдын алу мәселелері
Bibliographic record
Abstract
The article comprehensively examines the legal nature of vandalism, the degree of its social danger, and the specific features of its legal regulation in the Republic of Kazakhstan. The relevance of the study is обусловлена the increasing number of cases of damage to property in public places, desecration of historical and cultural heritage sites, and harm to public infrastructure. The article provides a legal analysis of the provisions of the Criminal Code of the Republic of Kazakhstan and the Code of Administrative Offenses of the Republic of Kazakhstan that establish liability for acts of vandalism. In addition, based on legal statistics, the dynamics of vandalism-related offenses in the country are examined. The study analyzes the main forms of manifestation of vandalism and its socio-psychological causes and proposes a scientific classification of its various types. The authors substantiate that vandalism is closely related to the level of legal culture in society, the characteristics of youth social behavior, and the degree of citizens’ responsibility for public property. The results of the study demonstrate that the prevention of vandalism requires not only strengthening legal liability but also the implementation of a comprehensive policy aimed at improving citizens’ legal culture, enhancing mechanisms for the protection of public property, and systematically implementing preventive measures.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".