Historical milestones in the formation of criminal policy as a science
Bibliographic record
Abstract
Исследованы отдельные исторические вехи становления уголовной политики как науки. Показаны источники, в которых впервые обсуждалась тема появления и развития уголовной политики как науки. Рассмотрены примеры формирования уголовной политики, начиная с петровских реформ первой четверти XVIII в., раннего советского, советского военного периода и заканчивая современным состоянием. Исторический метод исследования позволил выявить некоторые проблемы уголовной политики. Предложены общие подходы к их решению. Separate historical milestones in the formation of criminal policy as a science have been studied. The sources are shown, in which the topic of the emergence and development of criminal policy as a science was discussed for the first time. Examples of the formation of criminal policy are considered, starting with Peter's reforms of the first quarter of the 18th century, early Soviet, Soviet military period, and ending with the current state. The historical method of research has made it possible to identify some problems of criminal policy. General approaches to their solution are proposed.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| 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".