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‘Slimme Don’: de intelligente maffiabaas van Rusland

2017· article· en· W2779518986 on OpenAlexaboutno aff
D. Siegel

Bibliographic record

VenueJustitiële verkenningen · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geopolitical and Social Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPhilosophy

Abstract

fetched live from OpenAlex

‘The smart Don’: Russia’s intelligent mafia boss Drawing on material from her extensive study of Russian organized crime, the author sketches a portrait of Semjon Mogilevitsj. This Russian ‘godfather’ enjoys the reputation of being the smartest and highest educated mafia boss worldwide. Indeed Mogilevitsj obtained a master’s degree in Economics with good results, but this alone doesn’t explain his phenomenal successes in criminal as well as in legal business. Describing the steady expansion of his criminal activities from Russia into Israel, the United States, Eastern Europa, Italy and Canada, the author suggests that the social and economic circumstances in Russia just before and during the collapse of the Soviet Union might have served as an ideal playing ground for Mogilevitsj to develop the specific social intelligence and skills needed to successfully run a criminal organization.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.336
Teacher spread0.304 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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