In 2008, the San Francisco-based antitrafficking nonprofit organization Not for Sale launched a campaign advocating “backyard abolitionism,” training American citizens to seek out and identify victims of human trafficking as part of their everyday activities. Based on two years of ethnographic participant observation with two evangelical Christian human trafficking outreach projects in Southern California, this article examines the processes of what I term vigilante rescue in human trafficking. The enthusiasm around this brand of civilian vigilantism mirrors contemporary trends in urban governance, including community policing and civilian neighborhood patrol as modes of law enforcement engagement that operate outside the formal dictates of “state control.” The nonstate actors discussed in this paper are empowered not through professional skills or legal authority, but rather through merging American concern with human trafficking with moral panics concerning race, class, and migration as markers of sex trafficking. Situating new trends in human trafficking vigilante rescue within the extant literatures on neoliberal governance globally, this article argues that vigilante rescue enforces state goals of surveillance and policing of working-class immigrant women in Los Angeles. These activities further racial, gender, and class divides that extend sexual state politics and privilege criminal justice rather than social welfare solutions to human trafficking.
Not in My ‘Backyard Abolitionism’: Vigilante Rescue against American Sex Trafficking
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Implications of Cryptocurrency Energy Usage on Climate Change
Zhang, Dongna, Xihui Haviour Chen, Chi Keung Lau, and Bing Xu. 2023. “Implications of Cryptocurrency Energy Usage on Climate Change.” Technological Forecasting and Social Change 187: 122219.
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How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions
Bhutta, Neil and Hizmo, Aurel and Ringo, Daniel. 2022. “How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions.” FEDS Working Paper No. 2022-67, SSRN
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- Authors with Diverse Backgrounds