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Record W2959616058 · doi:10.4000/echogeo.17704

Illegal cannabis cultivation in Europe: new developments

2019· article· en· W2959616058 on OpenAlexaboutno aff
David Weinberger, Michel Gandilhon, Jalpa Shah, Nacer Lalam

Bibliographic record

VenueEchoGéo · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisLegislationDiversification (marketing strategy)Production (economics)BusinessEuropean marketCannabis sativaPolitical scienceEconomyInternational tradeEconomicsLawMedicineMarketing

Abstract

fetched live from OpenAlex

Herbal cannabis is one of the most consumed illegal drugs in Europe, with increasing local production. Illicit cannabis cultivation is not new to Western Europe. It first emerged on the scene in the 1970s, in the wake of the counter-culture following the 1968 Protests. Since then, it has gradually become more professional due to the increased diversification of involved actors, such as Organized Crime Groups (OCG), and the growing role of players linked to mafia organizations in both Italy and Albany. In Spain and France, where Moroccan resin has long dominated supply and demand, the cannabis market has seen a rise in herbal cannabis production. This, in turn, challenges the role of OCGs invested in resin importation. Yet, European marihuana production cannot be defined as a strictly criminal business. Small growers remain significant actors in production. This trend in production must be examined against evolving attitudes towards marihuana at the global level, linked especially to new legislation in the United States and in Canada.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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