Technology, normalisation and male sex work

Technological change, particularly the growth of the Internet and smart phones, has increased the visibility of male escorts, expanded their client base and diversified the range of venues in which male sex work can take place. Specifically, the Internet has relocated some forms of male sex work away from the street and thereby increased market reach, visibility and access and the scope of sex work advertising. Using the online profiles of 257 male sex workers drawn from six of the largest websites advertising male sexual services in Australia, the role of the Internet in facilitating the normalisation of male sex work is discussed. Specifically we examine how engagement with the sex industry has been reconstituted in term of better informed consumer-seller decisions for both clients and sex workers. Rather than being seen as a ‘deviant’ activity, understood in terms of pathology or criminal activity, male sex work is increasingly presented as an everyday commodity in the market place. In this context, the management of risks associated with sex work has shifted from formalised social control to more informal practices conducted among online communities of clients and sex workers. We discuss the implications for health, legal and welfare responses within an empowerment paradigm.

A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.