Quantification is particularly seductive in times of global uncertainty. Not surprisingly, numbers, indicators, categorizations, and comparisons are central to governmental and popular response to the COVID-19 pandemic. This essay draws insights from critical data studies, sociology of quantification and decolonial thinking, with occasional excursion into the biomedical domain, to investigate the role and social consequences of counting broadly defined as a way of knowing about the virus. It takes a critical look at two domains of human activity that play a (...) central role in the fight against the virus outbreak, namely medical sciences and technological innovation. It analyzes their efforts to craft solutions for their user base and explores the unwanted social costs of these operations. The essay argues that the over-reliance of biomedical research on “whiteness” for lab testing and the techno-solutionism of the consumer infrastructure devised to curb the social costs of the pandemic are rooted in a distorted idea of a “standard human” based on a partial and exclusive vision of society and its components, which tends to overlook alterity and inequality. It contends that to design our way out of the pandemic, we ought to make space for distinct ways of being and knowing, acknowledging plurality and thinking in terms of social relations, alterity, and interdependence. (shrink)

The COVID-19 pandemic invites a question about how long-standing narratives of alterity and current narratives of disease are entwined and re-enacted in the diagnosis of COVID-19. In this commentary, we discuss two related phenomena that, we argue, should be taken into account in answering this question. First, we address the diffusion of pseudoscientific accounts of minorities’ immunity to COVID-19. While apparently praising minorities’ biological resistance, such accounts rhetorically introduce a distinction between “Us” and “Them,” and in so doing produce new (...) and re-enact old narratives of alterity. Second, these unsubstantiated narratives thrive on fake news and scarcity of data. The second part of this commentary thus surveys the methods through which the COVID-19 test is administered in various countries. We argue that techniques used for data collection have a major role in producing COVID-19 data that render contagion rates among migrants and other minorities invisible. In the conclusion, we provide two recommendations about how COVID-19 data can instead potentially work towards inclusion. (shrink)

This article introduces the methodology of the ‘Ontology Explorer’, a semantic method and JavaScript-based open-source tool to analyse data models underpinning information systems. The Ontology Explorer has been devised and developed by the authors, who recognized a need to compare data models collected in different formats and used by diverse systems. The Ontology Explorer is distinctive firstly because it supports analyses of information systems that are not immediately comparable and, secondly, because it systematically and quantitatively supports discursive analysis of ‘thin’ (...) data models – also by detecting differences and absences through comparison. When applied to data models underpinning systems for population management, the Ontology Explorer enables the apprehension of how people are ‘inscribed’ in information systems: which assumptions are made about them, and which possibilities are excluded by design. The Ontology Explorer thus constitutes a methodology to capture authorities’ own imaginaries of populations and the ‘scripts’ through which they enact actual people. Furthermore, the method allows the comparison of scripts from diverse authorities. This is exemplified by illustrating its functioning with information systems for population management deployed at the European border. Our approach integrates a number of insights from early infrastructure studies and extends their methods and analytical depth to account for contemporary data infrastructures. By doing so, we hope to trigger a systematic discussion on how to extend those early methodical innovations at the semantic level to contemporary developments in digital methods. (shrink)