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The internet trade of counterfeit spirits in Russia – an emerging problem undermining alcohol, public health and youth protection policies?

2017· preprint· en· W2605661668 on OpenAlexaff
Maria Neufeld, Dirk W. Lachenmeier, Stephan G. Walch, Jürgen Rehm

Bibliographic record

VenueF1000Research · 2017
Typepreprint
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsCounterfeitHarmThe InternetBusinessConsumption (sociology)AdvertisingPublic healthAlcohol consumptionCommerceEnvironmental healthLawMedicineAlcoholPolitical scienceSocial scienceSociologyBiologyComputer science

Abstract

fetched live from OpenAlex

Counterfeit alcohol belongs to the category of unrecorded alcohol not reflected in official statistics. The internet trade of alcoholic beverages has been prohibited by the Russian Federation since 2007, but various sellers still offer counterfeit spirits (i.e., forged brand spirits) over the internet to Russian consumers, mostly in a non-deceptive fashion at prices up to 15 times lower than in regular sale. The public health issues arising from this unregulated trade include potential harm to underage drinkers, hazards due to toxic ingredients such as methanol, but most importantly alcohol harms due to potentially increased drinking volumes due to low prices and high availability on the internet. The internet sale also undermines existing alcohol policies such as restrictions of sale locations, sale times and minimum pricing. The need to enforce measures against counterfeiting of spirits, but specifically their internet trade should be implemented as key elements of alcohol policies to reduce unrecorded alcohol consumption, which is currently about 33 % of total consumption in Russia.

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 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)0.002
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 score0.193
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.295
GPT teacher head0.435
Teacher spread0.140 · 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 teacher head, 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

Citations14
Published2017
Admission routes1
Has abstractyes

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