Bibliographic record
Abstract
As a result of the conducted research, it was found that the measures taken in the Russian Federation for the purpose of adaptation and re-socialization of former convicts are insufficient for the following reasons. Firstly, the psychological and educational work carried out with convicts is ineffective due to the lack of trust among the latter in the staff of the psychological service. Secondly, the specialties that a convict can master in a correctional institution are not relevant. Former convicts, being livestock breeders, turners and seamstresses, cannot represent a competitive force in the labor market. Thirdly, about a quarter of all crimes are committed by previously convicted persons. These facts indicate that the adoption of the Federal Law "On Probation in the Russian Federation" is a timely measure that can prevent further recidivism of crimes and adapt former convicts to life in society. However, the text of this law excludes the concept of "pre-penitentiary probation". However, it is necessary because: 1) a small number of citizens can afford to conclude an agreement with a lawyer due to the high cost of his services; 2) courts, choosing a measure of restraint on particularly serious articles, most often decide to detain the accused, while the accused cannot fully realize their legitimate interests and build a line of defense together with their lawyers.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".