The epistemic selectivity of online platforms: The case of data sharing for academic research on labour platforms

Digital labour platforms such as Amazon Mechanical Turk, Deliveroo, and Uber concentrate large volumes of socially relevant data while severely limiting independent research access, creating deep asymmetries in what can be empirically known about their operations. This paper provides the first systematic analysis of how these asymmetries shape academic knowledge production, drawing on 397 empirical studies of 57 labour platforms operating in Europe. We show that only 7% of studies rely on platform-provided data and that these are disproportionately concentrated on topics aligned with platform interests, such as sustainability and consumer welfare, while contentious issues such as labour rights, earnings, discrimination, and safety are overwhelmingly studied using user-reported or scraped data. We conceptualise this pattern as epistemic selectivity: a structural dynamic through which platform-controlled data environments selectively enable some research agendas while constraining others. The study advances current debates on business-to-science data asymmetry and platform observability by showing that platforms shape the contours of public interest knowledge itself and highlights the need for regulatory frameworks that ensure more equitable and accountable data access.