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Record W3047738908 · doi:10.1093/bjc/azaa054

Conflict Management in High-Stakes Illegal Drug Transactions

2020· article· en· W3047738908 on OpenAlexaff
Martin Bouchard, Melvin Soudijn, Peter Reuter

Bibliographic record

VenueThe British Journal of Criminology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBlameDatabase transactionConflict managementConflict resolutionProduct (mathematics)BusinessCriminologyEconomicsLaw and economicsPolitical sciencePsychologySocial psychologyLawComputer science

Abstract

fetched live from OpenAlex

Abstract We draw from aspects of Black’s theory of conflict management to (1) provide a description of the types of disputes occurring at the highest levels of the drug trade and (2) examine whether conflicts that end in violence differed from those that found a peaceful resolution. A mixed-methods approach was used to analyse 33 incidents of smuggling transaction failures at the highest levels of the cocaine trade in the Netherlands. The results show that outcomes were determined by the way in which conflicts arose; lost product due to negligence and fraud increased the likelihood of violence. The fragile equilibrium between peaceful or violent resolution depended on whether the guilty party accepted blame or provided evidence that could account for losses.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.067
GPT teacher head0.288
Teacher spread0.222 · 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 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

Citations11
Published2020
Admission routes1
Has abstractyes

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