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Record W3046381283 · doi:10.1080/17440572.2020.1799787

Distributing tobacco in the dark: assessing the regional structure and shipping patterns of illicit tobacco in cryptomarkets

2020· article· en· W3046381283 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 · 2020
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRevenueBusinessTax revenueTobacco industryNiche marketInternational tradeEconomicsPublic economicsMarketingPolitical scienceFinance

Abstract

fetched live from OpenAlex

The size of the global market for illicit tobacco products is estimated to be between USD$8.6 and USD$11.6 billion yearly. In addition to an estimated cost of USD$40.5 billion in lost tax revenue the illicit tobacco market further increases the accessibility of a harmful substance for minors and provides a revenue stream for both organised crime and violent political groups. In this paper, we examine how tobacco products are distributed globally through illicit online platform economies known as cryptomarkets. Using data from the cryptomarket Empire, we find tobacco products remain a small niche market exclusively shipping from the EU and that shipping patterns suggest the emergence of new supply routes for end-consumers within Western Europe originating from the UK. We find that the market for tobacco on cryptomarkets remains minimal, as in previous research, compared to the market for drugs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.365

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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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