Based on the "ethnic" factor to understanding the distinct characteristics of cannabis cultivation: A review of overseas Vietnamese drug groups
Bibliographic record
Abstract
To date, indoor growth is known to be the main method of cannabis cultivation around the world. Appearing and increasing' trend of this field in Vietnamese crime groups have become a considerable concern since recent times with both the potential yield and potency of the crop at some of nations and regions, including Australia, Canada and European countries such as the United Kingdom, the Netherlands, and Czech Republic as well. Meanwhile, the limited researches and official document from Vietnam's authorities focus on Vietnamese criminal at overseas, no except for five above countries that it is likely to lead to unbalance in researching and assessing the nature of Vietnamese drug trafficking networks. This study offers a review of recent English-language researches that focused on Vietnamese cannabis cultivation at overseas. All of empirical studies were identified based on literature searches using relevant search terms and Social Science Research Network, Springer, Taylor & Francis Groups, and Elsevier Science Direct. One of the main purposes of this study is identify and evaluate the ethnic factors in Vietnamese cannabis cultivation networks at five above countries. The paper is divided into three sections, the first one review background on the nature of drug trafficking networks and ethnical factor in that; the second is assesses the Vietnamese illegal cannabis cultivation networks at overseas; synthesizing main characteristic from all discussions and analyses is basic requirement of the third section.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".