Weird Solidarities, Karen Gregory, Digital Sociology, University of Edinburgh
Whereas past generations longed to know if there is an afterlife, today we face a living hauntology in the form of our data presences. We live on not only past death, as the recent Facebook end-of-year debacles have poignantly demonstrated, but we live beyond ourselves in and through black-boxed algorithms and their architectures of capture and deployment. While we might understand this as a form of posthumanism or by using the framework of human/machine relations, I suggest we think of it this way: as a form of “weird” solidarity not only with one another but with the very environments that are being made to be “expressive” (Thrift 2012) along with us. As value grows increasingly speculative, being drawn from the dual promise of data aggregation and its parsing—for data are only as valuable as the novel emergent patterns it can produce—such value is already predicated on a social body and the generative connections that can be forged among its constituent elements. These elements do not necessarily have to reduce to “the human.” Additionally, this is a laboring and productive body whether it “works” or not. In this way, this economy does not need “you,” but it is fully composed of “us.” This talk explores the potential of this weird solidarity to consider possibilities of social and racial justice in a data-ified world.
Karen Gregory is a Lecturer in Digital Sociology at the University of Edinburgh and programme director of the “Digital Society” MSc. She is co-editor of Digital Sociologies (Policy Press, 2016) and her research interests span digital labor in the sharing economy to the labor of digital scholarship in the University. Her recent publication “Can Tech Schools Go Cooperative” appears in Ours to Hack and to Own, ed. Trebor Scholz and Nathan Schneider (OR Books, 2016). unique ways that culture and biology interact in our species. He has pioneered a new approach to understanding cultural evolution of behaviours such as language which we call Iterated Learning.
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