Abstract
This paper focuses on the experiences of Latin American data workers who annotate data for machine learning algorithms through labor platforms. It introduces the notion of ‘embedded reproduction’: the relationship between embeddedness, the degree to which non-economic institutions and their social environment constrain socioeconomic activity, and social reproduction, or the activities that nurture, maintain, and regenerate the workforce. The analysis of 38 interviews with platform workers suggests they are situated in a highly disembedded market due to the lack of regulations on the data production process, giving free rein to platforms to set rules to their detriment. This article explores how this disembeddedness shapes social reproduction by studying three forms of collective social support received by workers: from family members, neighbors and local communities, and online groups. The support of these networks is primarily local, depends on high levels of trust, and is gendered. These findings suggest that platform data work is unsustainable from an embedded reproductive perspective since platform intermediation leads workers and local communities to carry out the social and economic risks associated with this form of gig work. This research invites a dialogue between the embeddedness framework with social reproduction as well as a consideration of the importance of nature and natural resources in the study of social environments.
Julian Posada
Assistant Professor
Research interests include platform labor, data production, and social computing.