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Record W1494857857 · doi:10.1002/9780470514245.ch4

Cocaine Problems in the Coca‐Growing Countries of South America

2007· review· en· W1494857857 on OpenAlexaff

Bibliographic record

VenueNovartis Foundation symposium · 2007
Typereview
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsMcGill University
Fundersnot available
KeywordsCocaAbandonment (legal)Language changeAgricultureBusinessCommodityPolitical scienceGeographyDevelopment economicsNatural resource economicsPolitical economyEconomicsLawMarket economyArchaeologyArt

Abstract

fetched live from OpenAlex

The problems of cocaine present a rather particular profile in the Central Andes region from which this drug originates. On the one hand there is a relatively harmless pattern of use (coca leaf chewing) in the countries concerned which minimizes the drug's most hazardous properties. On the other hand the region suffers from some of the most severe cocaine-related problems to be observed anywhere: (a) easy access to the newer, highly toxic preparations of the drug (such as coca paste) and a rapid growth in the number of new users; (b) the abandonment of certain traditional and essential agricultural activities in favour of the more profitable coca leaf production; (c) the severe ecological damage being caused in the coca growing areas; and (d) the establishment of a powerful coca trade economy which is subverting the very fabric of society and is creating corruption, lawless violence and political anarchy.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.084
GPT teacher head0.380
Teacher spread0.296 · 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
GenreReview

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

Citations12
Published2007
Admission routes1
Has abstractyes

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