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Record W2017262854 · doi:10.1093/jicj/mqr005

Why and How to Make an International Crime of Medicine Counterfeiting

2011· article· en· W2017262854 on OpenAlexaff
Amir Attaran, Roger Bate, Megan Kendall

Bibliographic record

VenueJournal of International Criminal Justice · 2011
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Quality and Counterfeiting
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCounterfeitTreatyJurisprudenceCounterfeit DrugsHumanityPolitical scienceCurrencyInternational lawLawLaw and economicsBusinessCriminologySociologyEconomics

Abstract

fetched live from OpenAlex

The article explores why — when the counterfeiting of medicines is so prevalent, hard to detect and quietly dangerous or fatal — it remains totally unaddressed and therefore legal in international criminal law. It is argued that criminalizing the counterfeiting of medicines on an international scale would present no legally insurmountable barriers, and would offer significant advantages over the current national-scale approaches. The authors propose a legal definition of ‘counterfeit’, canvass the current legal doctrines that could be arrayed to better criminalize medicine counterfeiting, including classifying the severest instances as crimes against humanity, and explain the mechanisms necessary to close the jurisdictional gaps that are currently exploited by organized criminals who trade in counterfeit medicines across borders. They suggest that a counterfeit medicine treaty should be drafted under the auspices of the World Health Organization, and illustrate the feasibility of doing so with existing and developing treaty law on another health danger, 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.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.166
GPT teacher head0.414
Teacher spread0.247 · 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 designBench or experimental
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

Citations11
Published2011
Admission routes1
Has abstractyes

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