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Record W4382240986 · doi:10.1093/bjc/azad027

Illegal Market Governance and Organized Crime Groups’ Resilience: A Study of The Sinaloa Cartel

2023· article· en· W4382240986 on OpenAlexaff
Valentin Pereda, David Décary-Hêtu

Bibliographic record

VenueThe British Journal of Criminology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsUniversité de MontréalInternational Centre for Comparative Criminology
Fundersnot available
KeywordsCartelCorporate governanceResilience (materials science)Organised crimePsychological resiliencePoliticsTerrorismBusinessPolitical economyPolitical scienceEconomicsLawFinancePsychologyIndustrial organization

Abstract

fetched live from OpenAlex

Abstract Since its emergence in the early 1990s, the Sinaloa Cartel has effectively surmounted all challenges to its existence, while, simultaneously, successfully developing its illegal ventures in Mexico and beyond. Based on evidence from the accounts of witnesses who testified in the prosecution of Joaquin Guzmán Loera (also known as El Chapo), one of the Sinaloa Cartel’s most prominent figures, we argue that this OCG’s resilience partially derives from the illegal governance practices it has implemented in the criminal markets in which it operates. In particular, we contend that the Sinaloa Cartel’s reliance on four types of illegal governance have been pivotal in promoting its capacity to weather adversity, namely: (1) judicial, (2) financial, (3) political and (4) regulatory governance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.994

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.286
Teacher spread0.253 · 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 designObservational
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

Citations10
Published2023
Admission routes1
Has abstractyes

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