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Record W4380521779 · doi:10.6000/1929-4409.2020.09.326

Terms of Citizens 'Rights Restriction when Applying Measures of Criminal Procedure Forcing

2020· article· en· W4380521779 on OpenAlexvenueno aff
E.A. Askat, B.E. Zhaksybayev, Arman SAKHARBAY, A.M. Nurbeko, Т.А. Ханов

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCoercion (linguistics)Principle of legalityLawValue (mathematics)Criminal procedurePolitical scienceProcess (computing)Criminal lawAction (physics)Property (philosophy)Law and economicsPsychologySociologyComputer scienceEpistemology

Abstract

fetched live from OpenAlex

The article considers the nature and purpose of measures of criminal procedure coercion. The authors highlighted the grounds for restricting constitutional rights and freedoms when using coercive means. The author’s definition of measures of criminal procedural coercion is formulated, which means: the preventive action applied by the authorities conducting the criminal process in the criminal case aimed at achieving the objectives of the pre-trial investigation and trial or ensuring proper conduct of the participants in the criminal process specified in the law, if any conditions and circumstances that necessitate the use of such an impact. The issues of legality and localization criteria of personal property and non-property subjective interests of citizens are raised. The analysis of the main classifications of measures of procedural coercion is carried out, and the author's approach to the classification of measures of procedural coercion is proposed, based on the conditions and the real need to limit the rights and legitimate interests of the person. Article materials can be of practical value for employees of investigative units conducting pre-trial investigations and encountering problems with the application of procedural coercive measures.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.140
GPT teacher head0.370
Teacher spread0.230 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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