Bibliographic record
Abstract
Purpose This paper aims to analyze drug counterfeiting, explains its risk factors and operating and legal environments reviews recent legal cases and develops a multi-stakeholder prevention strategy that includes forensic accounting methods. Design/methodology/approach This is a theoretical study based on legal case studies and the best forensic accounting strategies. Findings Pharmaceutical drug counterfeiting is a fast-growing fraud that so far has attracted little attention from forensic accountants. A recent estimate projects that criminals collect around $75bn annually in illicit sales from counterfeit drugs (Bairu, 2015). Pharmaceutical counterfeiting also leads to the loss of lives when criminals use lethal chemicals in the manufacturing of fake medicines (Liang, 2006a; Brown, 2005). Because the detection of drug counterfeiting is extremely difficult after fake medicines have been ingested by patients, the strategy developed in this paper is based on early discovery by using reliable tracking technologies and inventory management controls in the supply chain, conducting effective regulatory and legitimate customs inspections, and increasing consumer awareness of basic forensic accounting tools. Research limitations/implications This paper extends previous research by integrating various factors into a single multi-stakeholder prevention framework. Practical implications The paper presents a synthesized, comprehensive view of the drug fraud epidemic and analyzes concrete steps that can be taken to protect the pharmaceutical supply chain to reduce the loss of lives and monetary injuries. Originality/value No previous research has analyzed this issue from a multi-stakeholder point of view and used forensic accounting tools to complement a prevention strategy. The drug counterfeiting prevention strategy developed in this paper addresses the supply side, the regulatory enforcement side and the demand side.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".