Stopping Murder by Medicine: Introducing the Model Law on Medicine Crime
Bibliographic record
Abstract
The iatrogenic pandemic of untreated illness related to falsified and substandard medicines is intolerable, but has a logical explanation: in many countries, inadequate laws make it barely illegal to manufacture or distribute poor-quality medicines. The law hardly punishes those who intentionally or recklessly deal in falsified or substandard medicine, when clearly it should criminalize these perpetrators in proportion to the grievous--even fatal--injury they inflict on public health. To solve this omission, this article presents a new Model Law on Medicine Crime, which countries may freely use as a template for strengthening their national laws. The Model Law includes criminal prohibitions against manufacturing, trafficking, or selling poor-quality medicines; principles for appropriately punishing offenders; special provisions for Internet-based medicine crimes; tools for encouraging whistle-blowers to cooperate with law enforcement; incentives for developing governments to strengthen their drug regulatory capacity; and important exceptions to prevent the law being abused, such as to prevent the prosecution of legitimate medical researchers or to prevent good-quality generic medicines being seized while in transit. The Model Law is discussed and explained and is offered free of charge under a Creative Commons license to any governments wanting to implement it.
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 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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".