The global diversion of pharmaceutical drugs
Bibliographic record
Abstract
AIMS: This paper explores India's role in the world illicit opiate market, particularly its role as a producer. India, a major illicit opiate consumer, is also the sole licensed exporter of raw opium: this unique status may be enabling substantial diversion to the illicit market. METHODS: Participant observation and interviews were carried out at eight different sites. Information was also drawn from all standard secondary sources and the analysis of about 180 drug-related criminal proceedings reviewed by Indian High Courts and the Supreme Court from 1985 to 2001. FINDINGS: Diversion from licit opium production takes place on such a large scale that India may be the third largest illicit opium producer after Afghanistan and Burma. With the possible exceptions of 2005 and 2006, 200-300 tons of India's opium may be diverted yearly. After estimating India's opiate consumption on the basis of UN-reported prevalence estimates, we find that diversion from licit production might have satisfied a quarter to more than a third of India's illicit opiate demand to 2004. CONCLUSIONS: India is not only among the world's largest consumer of illicit opiates but also one of the largest illicit opium producers. In contrast to all other illicit producers, India owes the latter distinction not to blatantly illicit cultivation but to diversion from licit cultivation. India's experience suggests the difficulty of preventing substantial leakage, even in a relatively well-governed nation.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".