Why and How to Make an International Crime of Medicine Counterfeiting
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".