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Record W2888149446 · doi:10.32370/2018_07_59

The International Search of Persons Committed a Terrorist Act

2018· article· en· W2888149446 on OpenAlexvenueno aff
Alimjan Matchanov

Bibliographic record

VenueIntellectual Archive · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUkrainian Legal and Forensic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLaw enforcementTerrorismOrder (exchange)EnforcementPolitical scienceComputer securityLawBusinessComputer science

Abstract

fetched live from OpenAlex

The article addresses on the issues related to the need to improve the search for terrorists and extremists, shows the main directions of the search activity of law enforcement agencies, examines the specifics of the criminal procedure, criminalistic support, operational tracking and international legal regulation of the search for hiding terrorists and extremists.At the same time, the article emphasizes that the knowledge of the bases of cooperation and coordinated actions of law enforcement agencies, international organizations in the implementation of the search is a necessary condition for the successful search of hiding terrorists.According to the author, it is necessary to know the features of the criminal procedure order, criminalistic tactics of international search of persons committed a terrorist act.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.041
GPT teacher head0.323
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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