LEGAL ASPECT OF THE FIGHT AGAINST SOCIAL EXCLUSION OF DRUG ADDICTS: INTERNATIONAL EXPERIENCE AND ADAPTATION IN KYRGYZSTAN
Bibliographic record
Abstract
В данной статье рассматривается юридический аспект борьбы с социальной эксклюзией наркозависимых, изучается международный опыт и его потенциальная адаптация к реалиям Кыргызской Республики. Проблема социальной изоляции наркозависимых связана со стигматизацией, ограничением прав и дискриминацией, что усугубляет их маргинализацию и препятствует успешной реинтеграции в общество. Основное внимание уделено правовым механизмам, применяемым в таких странах, как Португалия, Швейцария, Канада и Австралия, где реализованы эффективные стратегии, включающие декриминализацию, программы заместительной терапии и социальную поддержку. Особое место занимают вопросы правового регулирования и их роли в защите прав человека и преодолении дискриминации. В заключение представлены рекомендации по разработке и внедрению правовых инициатив в Кыргызской Республике, направленных на уменьшение социальной изоляции наркозависимых, улучшение доступа к реабилитации и успешную их интеграцию в общество. Данное исследование проводится в рамках проекта ОшГУ (Д.31-24) This article examines the legal aspect of the fight against social exclusion of drug addicts, studies international experience and its potential adaptation to the realities of the Kyrgyz Republic. The problem of social isolation of drug addicts is associated with stigmatization, restriction of rights and discrimination, which aggravates their marginalization and hinders successful reintegration into society. The main attention is paid to the legal mechanisms used in countries such as Portugal, Switzerland, Canada and Australia, where effective strategies have been implemented, including decriminalization, substitution therapy programs and social support. A special place is occupied by issues of legal regulation and their role in protecting human rights and overcoming discrimination. In conclusion, recommendations are presented for the development and implementation of legal initiatives in the Kyrgyz Republic aimed at reducing the social isolation of drug addicts, improving access to rehabilitation and their successful integration into society. This study is being conducted within the framework of the Osh State University project (D.31-24)
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".