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Record W621690391 · doi:10.7202/703805ar

Kaléidoscope : l'instrumentalisation du problème de la drogue dans les Amériques

2005· article· en· W621690391 on OpenAlexaffvenue
Michel Fortmann, Guillermo R. Aureano, Michel Lopez

Bibliographic record

VenueÉtudes internationales · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsUniversité de MontréalBureau de Coopération Interuniversitaire
Fundersnot available
KeywordsDrug traffickingPoliticsPhenomenonPolitical scienceFrame (networking)Work (physics)Welfare economicsCriminologySociologyLawComputer scienceEpistemologyEconomicsEngineeringPhilosophy

Abstract

fetched live from OpenAlex

The purpose of this article is to shed new light on the issue of drug trafficking in the Americas. Specifically its objective is to demonstrate that the perception of drug trafficking as a national security threat is only one aspect of a much more complex socio-political phenomenon. First, this paper presents the multilateral mechanisms that serve as a frame work to facilitate regional cooperation on drug matters. It goes on to analyze the different approaches of regional drug trafficking. It concludes that a much more flexible approach is needed to gain an in-depth understanding of drug politics in the Americas.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.307
Teacher spread0.288 · 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
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

Citations2
Published2005
Admission routes2
Has abstractyes

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