MétaCan
Menu
Back to cohort

Historical milestones in the formation of criminal policy as a science

2023· article· ru· W4390268231 on OpenAlexaboutno aff
Л.В. Голоскоков

Bibliographic record

VenueРасследование преступлений проблемы и пути их решения · 2023
Typearticle
Languageru
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPeriod (music)Political scienceState policyState (computer science)Quarter (Canadian coin)LawHistoryCriminologyPolicy analysisSociologyPhilosophyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.013
Science and technology studies0.0020.003
Scholarly communication0.0000.002
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.093
GPT teacher head0.418
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueРасследование преступлений проблемы и пути их решенияSame topicLegal and Policy IssuesFrench-language works237,207