Альтернативні Санкції Та Проблеми Їх Сприйняття Суспільством І Втілення в Україні (Alternative Sanctions and the Problems of Their Community Perception and Implementation in Ukraine)
Bibliographic record
Abstract
Ukrainian Abstract: Розглядаються можливості широкого запровадження альтернативних санкцій. Висвітлюється позитивний досвід іноземних країн, зокрема, Канади, Німеччини, Фінляндії, Швеції, з втілення пробації. Надаються аргументи, що політика держави повинна бути спрямована на переконання суспільства у недоцільності і шкідливості існуючих нині підходів. Зазначається необхідність роз’яснення спроможності виконувати інші, альтернативні види покарань, що зменшить вірогідність кримінального зараження суспільства. Акцентується увага на доцільності розповсюджувати досвід цивілізованих країн. English Abstract: The possibilities of the widespread introduction of alternative sanctions in judicial practice are researched. The positive experience of foreign countries in the sphere of implementation of probation is demonstrated. In particular, the examples of Canada, Germany, Finland and Sweden in the field of combating crime and the application of alternative sentences, are researched. It is noted that the increase of the crime rate forms among in the overwhelming majority of the population a view about the need for a tougher response to crimes. The penalty is considered to be the better, the more limitations and suffering it contains.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".