Call For Papers AAG 2026: Platform Labor: The Geographies, Economies and Inequalities under Algorithmic Management and Big data

CFP Deadline: 28th October

In-person session

Organizers:

• Balaji Parthasarathy, International Institute of Information Technology Bangalore
• Raktima Kalita, University of Kentucky

Session Chair: Mark Graham, University of Oxford

Digital platforms facilitate work using “digital technologies to “intermediate” between individual suppliers (platform workers and other businesses) and clients, or directly engage workers to provide labor services” (ILO, 2021: 33). The work undertaken on these platforms is also commonly referred to as ‘platform work’ .The vast majority hired for platform work perform platform-mediated individual ‘gigs’ or tasks, such as last-mile delivery, and are categorised as self-employed or independent contractors. Gig work is a “non-standard form of employment”, that designates all employment that is definite, and neither fulltime nor part of a subordinate and bilateral employment relationship (ILO 2016). This call is specifically for physical or geographically tethered gig work: location-based digital labor or “work on-demand via apps” (De Stefano, 2016:1).

Technological advances such as big data, algorithmic management, and artificial intelligence have led to the growth of the platform economy, reshaping the geographies of capital and labor (Graham et al., 2017). Platforms and their infrastructures have enabled lowering of transaction costs, space-time compression (Harvey, 1990) and new forms of work opportunities that rely on flexible on-time labor. The new forms of work that workers experience are, however, largely dictated by the platforms. Based on slogans such as ‘be your own boss’, ‘work as much as you want to’ or ‘you choose when you deliver’, platforms argue that gig workers, or last mile platform workers, enjoy ‘flexibility’ (Cano et al, 2021) even while using the the term as an antidote to labor-market ‘rigidities’ such as restricted work hours, minimum wage, health/safety protections, and right to collective bargaining. As Massey (2005) reminds us, processes of space-time compression are inherently embedded within social identities and hierarchical relations among stakeholders; and, as Cano et al (2021) ask, “flexibility for whom?” Work in platforms is associated with low wages, increased accident risks, lack of social security benefits, poor grievance redressal, discrimination, emotional labor, and limits to collective bargaining.

Platforms control workers through a black box of algorithmic management (Rosenblat & Stark, 2016) which renders opaque in the way wages are determined, work flow and worker behaviour is managed, and grievances are redressed. Algorithmic management has also been called “taylorism on steroids” (Naponen et al, 2023). These structural shifts have questioned the notion of autonomy in platform work (Muldoon & Raekstad, 2023, Wood et al, 2019) and has been argued to have increased alienation (Kassem, 2023) from the labor process, from other workers, and from the product of the labor. Alienation is also deeply intertwined with the discrimination that workers face due to social identities such as gender, caste, class and religion Fairwork India (2023).

Against the backdrop of these discussions, we invite abstracts, both empirical and theoretical, from studies on geographically tethered platform work, conducted in any part of the world on the following themes (including, but not limited to):

1. Wages and compensation
2. Conditions of work, safety and risk, mental and physical health, social security
3. Contracts, independent contractors, non standard form of employment
4. Algorithmic management, grievance redressal, discrimination, exclusion, alienation
5. Collective bargaining and politics of representation
6. Spatial control and spatiality of platform workers, urbanism

To submit your abstract for this paper session, please send your abstract (250 words or less) to Raktima.Kalita@uky.edu by October 28th.

You may continue to revise and edit your abstract until November 10th, but we ask that you submit an initial version by the earlier date to meet the official deadline.