Framing datafication as new form of knowledge production has become a trope in both academic and commercial contexts. This special theme examines and ultimately rejects the familiar grand claims of datafication, to instead pay attention to emergent conversations that seek to take a more nuanced stock of the status and nature of datafied knowledge production. The articles in this special theme thus engage with datafied knowledge production through elaborate explorations of how datafied knowledge depends on the contexts of its production (...) and the forms of knowledge production that precede it in those contexts. Our basic argument is that while the resources, material features and analytical operations involved in datafied knowledge production may be different, many fundamental concerns about epistemology, ontology and methods remain relevant to understand what shapes it. We still need to understand and explicate the assumptions, operations and consequences of emergent forms of knowledge production. If datafied knowledge production is neither a clean revolutionary break with past forms of knowledge production nor a balloon of pure hype, the articles in this special theme ask: what does the phenomenon of datafied knowledge production look like? Which digital and datafied infrastructures support its future development? And what potentialities and limits do such forms of analysis and knowledge production contain? (shrink)

As states increasingly use algorithms to improve the legibility of society, particularly during the COVID-19 pandemic, it is common for concerns about the expanding power of the algorithm or the state to be raised in a deterministic manner. However, how are the algorithms for states’ legibility projects enacted, contested, and reconfigured? Drawing on interviews and media data, this study fills this gap by examining Health Code, the Chinese contact tracing and risk assessment algorithmic system that serves as the COVID-19 health (...) passport. I first explore the intensive and invisible work and infrastructures that enact and stabilize Health Code’s sociotechnical assemblage. I then show how this assemblage is frequently challenged and destabilized by errors, breakdowns, and exclusions. Facing unintended engagements from heterogeneous social actors, local interests, and power hierarchies, Health Code reassembles into multiple and contradictory assemblages at different periods and social localities. Finally, I examine how people game and bypass the algorithm’s surveillance with their agencies. Recognizing this messiness and heterogeneity contributes to a more nuanced and realistic understanding of states’ use of algorithms, including the risks. Doing so also urges us to rethink the politics of citizenship and inequality in the digital age beyond inclusion. (shrink)

The Central American migrant caravans of 2018 are best understood as having been precipitated by entangled multi-scalar geopolitical histories among the United States, Mexico, Guatemala, Honduras, and El Salvador. Unsurprisingly, the migrants traveling north to the United States garnered widespread attention on social media. So much so that the reaction to the caravan accelerated plans to deploy troops to the US southern border and deny Central Americans the opportunity to seek asylum. This example showcases how the digital world can have (...) exponential material effects. While coverage on border security and migration has been extensive, within political geography, such concerns have rarely been paired with social media. In this article, we take as our object of analysis the digitality or “digital life” of the migrant caravan. Mapping the patterns of migrant caravan-related tweeting paired with the exploration of Twitter’s networked dimensions reveals the platform to be a fundamentally spatial technology. Rather than reflect, refract or distort, Twitter produces and is in turn produced through spatial mechanisms. We present multiple cartographic visualizations in support of this claim and highlight the ways in which a contextual knowledge of the subject under study—the migrant caravan—can further inform analyses of Big Data. (shrink)