The Purpose of "Correction" in the Russian Criminal and Criminal and Executive Law
Bibliographic record
Abstract
The process of carrying out (serving) a criminal punishment in the form of imprisonment according to the Russian criminal and penal law provides for isolation of the convicted person, limitations of rights and certain freedoms, as well as using corrective measures to change the criminal orientation of that person towards positive law-abiding behavior. According to penal law, the corrective process is being implemented by carrying out the main measures of corrective action, which the law lists as: regulated conditions, socially useful labor, educational work, general and professional education, and social influence. Currently the penal system sees a general trend of reducing the total number of imprisoned persons. According to the statistics by the Federal Penitentiary Service of Russia, 880 thousand people were serving a prison sentence in 2010, 550 thousand people in 2016, as of 1 June 2018 that number was 520.5 thousand people, and as of 1 May 2019 it was 552,188 persons. In the recent years, the number of prisoners tends to stay on the same level. These numbers testify to the continued humanization of the criminal and penal policy of the Russian Federation, as well as to the results of applying corrective actions to those sentenced to imprisonment.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.062 |
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".