Bibliographic record
Abstract
This essay provides a brief overview of arms trafficking, its participants and enabling partners, and the prospects of a reduction in arms trafficking through various legal mechanisms. It looks at two prominent arms traffickers—Viktor Bout and Leonid Minin—to illustrate these discussions. The trade in arms becomes arms trafficking when the deals undertaken violate existing laws on the movement of arms. These laws usually take the form of domestic licensing requirements or international arms embargoes. Arms trafficking is a complex multifaceted crime, and it can involve numerous discrete crimes over and above breaking arms sanctions, including, but not limited to, fraud, corruption, money laundering, smuggling, intimidation, and murder. Arms trafficking is undertaken in the context of the global trade in arms, made up of the formal world of legal trade and the “shadow world” of illegal transactions. Often actors operate in both worlds, and both can be mutually supportive in various ways. The number of collaborators involved in assisting arms traffickers means that successful prosecution of these traffickers is incredibly rare: of more than 500 United Nations arms embargo violations, only one participant has ever been convicted. The prospects for an end to arms trafficking are bleak. Not only are existing legal mechanisms flimsy, there seems to be little political will to develop an international framework that would help legal authorities pursue law-breakers. In addition, many countries are already awash in arms, providing ample opportunity for individuals to easily establish themselves as arms traffickers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.106 | 0.040 |
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".