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Record W2915036452 · doi:10.1080/17440572.2019.1569520

Size and scope of the tobacco trade on the darkweb

2019· article· en· W2915036452 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Crime · 2019
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsScope (computer science)Tobacco controlBusinessThe InternetIllicit drugDistribution (mathematics)Tobacco industryAdvertisingPolitical scienceMedicinePublic healthDrugComputer scienceLaw

Abstract

fetched live from OpenAlex

Globally, tobacco is responsible for the death of approximately 6 million individuals per year. In reaction, governments have enacted tobacco control policies that cope with the harmful effects of the drug. However, these policies have also created geographic price disparities and increased the effort needed to purchase legal tobacco products. The result was the creation of a new illicit tobacco market. While tobacco smuggling continues through conventional avenues, a new generation of traffickers have turned to a different source for the illicit distribution of tobacco – the internet, specifically the darkweb. The purpose of this paper is to describe the size and scope of illicit trafficking of tobacco on cryptomarkets, and the diverse types of tobacco-related products available on the cryptomarkets. Our analysis will provide guidance for policymakers, researchers, and all interested in the illicit trade of tobacco.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.955
Threshold uncertainty score0.165

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 science0.0010.000
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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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