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Record W4309151799 · doi:10.1142/s2630531322500093

Conflict and Crime: Will the Russia–Ukraine Conflict Result in the Second Rise of the Russian Mafia?

2022· article· en· W4309151799 on OpenAlexaff
Alexandra V. Orlova

Bibliographic record

VenueChinese Journal of International Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSanctionsPolitical scienceOrganised crimeState (computer science)Political economyRhetoricEconomic sanctionsMomentum (technical analysis)EconomyLawSociologyEconomics

Abstract

fetched live from OpenAlex

This paper deals with the question of whether Russia will experience the second rise of the mafia akin to the experience of the 1990s during the current Russia–Ukraine conflict. It examines the necessary conditions for the rise of the mafia that were present in the 1990s, such as the absence of trust in the state, the demand for protection, and the supply of individuals willing to provide protection. The paper concludes that despite economic woes generated by Western sanctions, the conditions for the rise of the mafia are currently absent in Russia. Nevertheless, turbulent times frequently lead to an increase in organized criminal activity, which seems to be the case in Russia at this particular point in time. Furthermore, due to anti-Western rhetoric, the idea of organized crime as a foreign threat to Russia will likely continue to gain momentum.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.336
Teacher spread0.312 · 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 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
Published2022
Admission routes1
Has abstractyes

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