The Shift To Online Tobacco Trafficking
Bibliographic record
Abstract
The illicit trade of tobacco products is a global phenomenon that has impacted the public health, the public finances and the security of nation states. A wide array of actors is involved in the illicit trade of tobacco, ranging from the individuals who bring home a few extra cartons of cigarettes while on a trip to the organized criminal organization of the mafia or terrorist type. The latter appears to be responsible for a very significant part of the illicit tobacco trade. While organized traffickers have traditionally used ports and roads to carry out their activities, a new generation of traffickers has turned to the Internet as a distribution channel for tobacco products. These traffickers are active on online illicit marketplaces known as cryptomarkets. While cryptomarkets have developed expertise in the sale of illicit drugs online, they are at the same time diversifying their activities into many new areas such as financial fraud, identity fraud and tobacco trafficking. The general aim of this paper is to describe and understand the shift to cryptomarkets in tobacco trafficking. Our results demonstrate that tobacco traffickers are involved in polytrafficking and that some do generate important revenues from their online illicit activities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".