Guns for hire: North America’s intra-continental gun trafficking networks
Bibliographic record
Abstract
Objective:to summarize and study the North America’s intra-continental gun trafficking networks. Methods:the work applies social network analysis (SNA) to understand structures, identify brokers and discover patterns in the way guns are being procured, transported across the border, and further distributed. Results:Since Canada adjoins the largest weapons market in the world, it is unsurprising that guns used to commit criminal acts in Canada largely originate in the United States. But how are such weapons transported across the border: by individual entrepreneurs, by small networks, or by sophisticated cartels? This article analyzes six cases that resulted in prosecutions of 40 Canadian and American citizens implicated in Canada-U.S. gun trafficking networks between 2007 and 2010. This study is a plausibility probe that applies social network analysis—investigating networks that come into existence by the creation of pairwise links among their members—to analyze global structures, identify brokers and their roles, and discover patterns in the way guns are being procured in the United States, transported across the border, and distributed in Canada. Scientific novelty: In the process, this study generates hypotheses about network structure and works towards modeling these networks functionally: Since guns are available legally in the United States, we expect to find a proliferation of relatively simple networks. In contrast, drugs, which are not as readily available, might require more sophisticated networks to be trafficked across the border. Results revealed that the trafficking network structures seem to be driven by function. When the objective of the network is mere rent-seeking, transborder trafficking networks for guns tend to be simple. By contrast, when the objective is to manage violence as a constituent element of a larger criminal organization and its activities, networks tend to be more sophisticated, although the gun trafficking networks remain simpler. Practical significance: the main provisions and conclusions of the article can be used in scientific, pedagogical and law enforcement activities when considering the issues related to the illegal firearms trafficking through the US and Canadian territories.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".