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Record W2800654834 · doi:10.1080/17440572.2018.1460951

Illicit payments for illicit goods: noncontact drug distribution on Russian online drug marketplaces

2018· article· en· W2800654834 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 · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Illicit Activities, and Governance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPaymentIllicit drugBusinessInternet privacyThe InternetEnforcementDistribution (mathematics)DrugLaw enforcementStreet drugsConsumption (sociology)CommerceAdvertisingComputer securityMarketingComputer sciencePharmacologyFinanceMedicineLawWorld Wide Web

Abstract

fetched live from OpenAlex

The distribution or consumption of traditional drugs has become the subject of stringent penalties throughout most of the world and synthetic designer drugs have become the alternative. Novel psychoactive substances, also called ‘legal highs’, are highly varied in terms of chemical composition. These substances are advertised and distributed as an alternative to traditional drugs on the Internet, making identification of new substances and enforcement difficult. For this article, we downloaded and analysed 28 Russian-language online drug marketplaces which distribute traditional and novel psychoactive substances. All marketplaces used a noncontact drug dealing method where the seller and the buyer communicate through the Internet to arrange for payment and delivery of drugs without meeting face-to-face. Geographic information, price, amount, substance type and payment method data were extracted. Findings indicate such marketplaces are able to operate due to the ability of their clients to pay anonymously with the virtual currencies – Qiwi and Bitcoin.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.330
Teacher spread0.309 · 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