Peer Reviewed

Peer Reviewed

Food delivery platforms are location-based services that rely on minimal, quantifiable data points, such as GPS location, to represent and manage labor. Drawing upon an ethnographic study of food delivery work in India during the COVID-19 pandemic, we illustrate the challenges gig workers face when working with a platform that uses their (phone’s) GPS location to monitor and control their movement. Further, we describe how these, along with the platform’s opaque, location-based logics, shape the delivery workflow. We also document how the platform selectively represented workers’ bodies during the pandemic to portray them as safe and sterile, describing workers’ tactics in responding to issues arising from asymmetric platform policies. In discussion, we consider what we can learn from understanding gig workers as ‘infrastructure’, commonly overlooked but visible upon breakdown. We conclude by reflecting on how we might center gig workers’ well-being and bodily needs in design.

We examine how two prominent food delivery platforms in India, Swiggy and Zomato, produce a managed digital workforce using a combination of algorithmic control and traditional labor management strategies. Our findings draw from interviews conducted with 13 food delivery workers and a critical discourse analysis of news media coverage. We found that the two platforms combine piece wage restructuring, granular datafication practices, and the use of benevolent language as neoliberal social control mechanisms. We find that this combination of technological governance and strategic managerial practices is a mutually constitutive method of control that restructures labor processes, extracts workers’ compliance and consent, and prevents work disruption. We show that contemporary platform companies draw from strategies that have historically been deployed in industrial labor management. By examining how older and newer regimes of social control and exploitation are strategically intertwined in contemporary platform design, we contribute a historically situated understanding of platform labor that moves beyond dualistic interpretations of “traditional” labor management practices and more recent algorithmic modes of control. Our findings contribute to recent debates in tech labor and algorithmic control by examining how contemporary conditions of precarious work reactivate certain past forms of control and in doing so normalize extreme overwork, exhaustion, speedups, and injuries.

Critical refusal is an active process; an informed practice of investigating power differences in order to generate more just and equitable alternatives to the status quo. In this paper, we examine what it means to utilize critical refusal as a tool for investigating unequal power dynamics that are produced and reified by data practices. We illustrate the generative capacity of critical refusal by drawing on declarations from The Feminist Data Manifest-No to examine data practices across three real-world cases. By pairing a conceptual exploration of critical refusal with real-world examples, we make a theoretical contribution that is grounded in concrete approaches for generating alternative data practices in ways that account for interlocking struggles across contexts and communities.