Narcotics Trafficking in China: Size, Scale, Dynamic and Future Consequences
Bibliographic record
Abstract
abuse and trafficking are not new developments in China. They have been part of China's history even prior to the Boxer Rebellion and the first British Opium War in the mid-1 800s, as the People s Republic of China s (PRC) sheer size and geography has traditionally made it a likely victim of drug trafficking.1 Today, largely because of China's increased trade and commercial interests with Southeast Asia, a loosening of travel restrictions within China, increased urbanization, longer work hours, and the gradual development of a more consumer-oriented society, the PRC has become a major transit point not only for narcotics bound for other Asian nations, but also for North America, Russia, Europe, Africa and Latin America.2 The repercussions for China have been severe. According to the UN, the PRC produces 50 percent of methamphetamines (AST) in Asia, with most of this production occurring in the southeastern Guangdong province.3 AST drugs include methamphetamine hydrochloride (shabu) and crystal methamphetamine (ice), with much of it supplying markets in the Philippines, Japan and South Korea before entering the global trade through contacts with the Chinese diaspora.4 Further, over one million people are
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
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".