Illicit drug trafficking via postal services: A scoping review of economic-criminological context and estimation methods
Bibliographic record
Abstract
Efficient and interconnected logistics networks, like postal services, provide powerful tools for criminal groups to organize cross-border drug trafficking by exploiting legitimate infrastructure. Despite postal services becoming inadvertent facilitators in smuggling, focused research on this logistical channel is scarce and scattered due to its multidisciplinary, cross-country nature. This paper conducts a systematic scoping review to explore three primary questions: (1) how drug trafficking through postal services is conceptualized, (2) the estimated volume of these illicit flows, and (3) the methodologies employed to determine these estimates. Our scoping review, which covers studies from 2006 to 2024, identified 49 relevant articles. The findings indicate that the inherent characteristics of the postal system limit its detection capacity, thereby increasing its appeal for smuggling activities. With criminals thus increasingly shifting to online operations and postal remote shipments, cross-border enforcement agencies need to enhance their awareness of the cross-border illicit trade dimension and their responsiveness to the phenomenon, as well as deepen their digital readiness and ensure digital-based coordinated intelligence. This study emphasizes that the fragmentation of knowledge, combined with the multitude of players involved in monitoring and enforcement activities, demands tight transnational cooperation to ensure enforcement effectiveness and prevent digital asymmetries.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".