This is work that resulted from long-term collaboration with Criminal Intelligence Service Alberta that James started while working in the Leverhume-funded Project Collecting and analyzing secondary covert social network data (RPG-2013-140) at the Mitchell Centre for Social Network Analysis, and teaching at the LINKS Center for Social Network Analysis. Data on criminal collaborations and criminal group affiliations are based on Human Intelligence (HUMINT), Signals Intelligence (SIGINT), and Open-Source Intelligence (OSINT). The paper draws on theories in the fields of organisational studies and criminology, and, using Bayesian inference for multilevel ERGM, draws the conclusion that members of different outlaw motorcycle gangs are more likely to collaborate when they target the same marketplaces than other kinds of Organised Crime Groups.
A public access link to the article is provided here (expires 25 July)