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Short abstract:

This work presents preliminary findings an ongoing research using Situational Analysis to articulate a multi-perspective study on ways of knowing urban space mobilised by Uber. It addresses the challenges in dealing with disparate social worlds and with messiness and incompleteness found in data

Long abstract:

This contribution is based on an ongoing research incorporating Situational Analysis’ (SA) theory-method package to articulate a multi-perspective study on different ways of knowing urban space mobilised by Uber and its infiltration in particular contexts. My research engages with these more-or-less platformised epistemologies through three main entry points: 1)Uber’s engineering teams’ techno-corporate discourse; 2)External experts and researchers who, in different levels, engage with Uber’s and similar platforms’ data, software, and digital infrastructures; and 3)Uber drivers. These are explored through interpretive analysis of engineering blog posts, research papers, social media posts and interviews with external experts and drivers.

This paper aims to provide an overview of the research process and preliminary findings and address challenges encountered when tracing and articulating disparate knowledge ecologies within acute power asymmetries. While such disparities can be illustrated by the contrast between Uber’s data-fueled knowledge production and drivers’ grounded experiences in the city, they also extend to the collection and construction of research data itself: while Uber’s and experts’ knowledge is pre-packaged as such, both in the form of posts and papers and the partially formatted discourse found in interviews, drivers knowledge practices are discursively entangled in messier assemblages and emerge alongside embodied experiences of work, tiredness, hope, ambition, and vulnerability. Challenges also arise in trying to analytically bring together social worlds that, while actively engaging with different facets of the city and platforms, do not seem to interact with or think about each other very often.