IMPROVMENT OF ENSURING POST-PENITENTIARY ADAPTATION OF MINORS RELEASED FROM PENITENTIARY INSTITUTIONS
Bibliographic record
Abstract
In this article the author conducted the issues of the related to ensuring the post-penitentiary adaptation of minor convicts who have served a sentence of imprisonment and released from penitentiary institutions. In particular, the content and essence of the concept of post-penitentiary adaptation, the main goals and features of ensuring post-penitentiary adaptation of convicts released from penitentiary institutions are illuminated. Furthermore, the article analyzed the ongoing reforms in our country to ensure the social rehabilitation of convicts released from penitentiary institutions and also studied the theoretical views of scientists in this field, current problems of law enforcement practice. In additions, author conducted international legislation and the experience of advanced foreign countries in the field of criminal-execution punishments, such as the Russian Federation, Germany, Japan and Canada. As well as, offered on improvement of the acts regulating to ensuring post-penitentiary adaptation of minors released from penitentiary institutions in the field are provided.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".