Difficulties With Drug Conspiracies in Singapore: Can You Conspire to Traffic Drugs to Yourself?
Bibliographic record
Abstract
If Person A delivers drugs to Person B at the latter’s request, Person A is liable for drug trafficking—a serious offense in many jurisdictions. However, the liability of Person B for drug trafficking is unclear as much may depend on Person B’s intention with the drugs. The Singaporean Courts recently had to grapple with this issue in Liew Zheng Yang v. Public Prosecutor and Ali bin Mohamad Bahashwan v. Public Prosecutor and other appeals. Prior to these two cases, the position in Singapore was clear—Person B should be liable for drug trafficking as an accessory to Person A, in line with Singapore’s strong stance against drug offenses. However, since these cases, the Singaporean Courts have taken a contrary position and held that Person B may not be liable if the drugs were for his/her own consumption. This Article examines the law with respect to this drug conspiracy offense in Singapore, looking at its history, the primary legislation and similar cases. It also scrutinizes the judicial reasoning in the two cases above and considers whether this can be reconciled with the Courts’ prior position on the issue. In this analysis, the Article also investigates the position taken in other comparable common law jurisdictions—including the UK, Australia, Canada and the United States—and concludes that the Singaporean Courts’ reasoning in the aforementioned two cases may not be tenable and warrant a reexamination.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".