Governmentality gone wild: How the separation of sex workers from 'communities' contributes to violence against sex workers

Sex workers are members of our communities, whether they are local or national communities. In law, mainstream media representations, and research sex workers are positioned as outside of or in opposition to communities. Even within marginalized communities sex workers are excluded when appeals to respectability politics are made. In this thesis I analyze three analytic sites from three areas of social life. The first chapter performs a textual analysis of The Bedford Decision (2013) and the resulting Protection of Communities and Exploited Persons Act (2014) as an examination of law. The second chapter is an analysis of filmic discourse on community, sex workers, and violence in the mainstream film London Road (2015) as an examination of mainstream media. The third chapter draws upon empirical research, i.e. in-depth interviews with three current and former sex workers in Ottawa, Canada and analyzes the transcripts using interpretative phenomenological analysis (IPA) to center how sex workers’ understanding of their work, community, and the laws and policies that are supposed govern and protect them. In my preface and conclusion I discuss some of the ethical dilemmas I encountered while conducting this research. My findings suggest that sex workers are being positioned and understood as outside of communities in ways that contribute to violence against sex workers. The implications of this research suggest that people who speak in the name of communities—communities in the sense of local neighborhood communities, activist communities, and national communities—need to recognize that sex workers are part of their communities and be accountable to ensuring they are treated as members. Researchers who conduct research on sex work and sex workers need to be accountable to their participants and the impacts their research may have on laws and policies. Sex workers are an over-researched population yet their voices are largely misappropriated or silenced in popular research and policy debates.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.

Rationalizing spatial exploration patterns of wild animals and humans through a temporal discounting framework

Understanding the exploration patterns of foragers in the wild provides fundamental insight into animal behavior. Recent experimental evidence has demonstrated that path lengths (distances between consecutive turns) taken by foragers are well fitted by a power law distribution. Numerous theoretical contributions have posited that “Lévy random walks”—which can produce power law path length distributions—are optimal for memoryless agents searching a sparse reward landscape. It is unclear, however, whether such a strategy is efficient for cognitively complex agents, from wild animals to humans. Here, we developed a model to explain the emergence of apparent power law path length distributions in animals that can learn about their environments. In our model, the agent’s goal during search is to build an internal model of the distribution of rewards in space that takes into account the cost of time to reach distant locations (i.e., temporally discounting rewards). For an agent with such a goal, we find that an optimal model of exploration in fact produces hyperbolic path lengths, which are well approximated by power laws. We then provide support for our model by showing that humans in a laboratory spatial exploration task search space systematically and modify their search patterns under a cost of time. In addition, we find that path length distributions in a large dataset obtained from free-ranging marine vertebrates are well described by our hyperbolic model. Thus, we provide a general theoretical framework for understanding spatial exploration patterns of cognitively complex foragers.