My work focuses on complexity and transition in knowledge infrastructures. Together with my colleagues in the Knowledge Infrastructures Department, I conduct multi- and transdisciplinary research to study, develop and innovate knowledge infrastructures, with a focus on the areas of climate change and ecology. I am co-author of Data and Society: A Critical Introduction (Sage, 2022), of Smart Grids from a Global Perspective (Springer), and of Virtual Knowledge: Experimenting in the Humanities and Social Sciences (MIT Press). My current work will be published as Revealing Relations: Knowledge Infrastructures for Liveable Futures, at Bristol University Press.
During a joint Fellowship with the Institute for Advanced Study and the Data Science Centre of the University of Amsterdam, I explore how interfaces can support epistemic diversity.
Contributing to beneficial disruptions in knowledge production
The central question is whether creating new kinds of interfaces can help diversify users and do more justice to existing diversity in data. This work contributes to the efforts of the IAS and DSC to examine how the science system can come up with surprising theories, wild ideas, new methods and innovative techniques that help to deal with major societal challenges and wicked problems. The hope is that novel knowledge infrastructures that include better interfaces can support team science and interdisciplinary work, and contribute to beneficial disruptions.
Building Knowledge Infrastructures for Liveable Futures
Current knowledge infrastructures serve a narrow set of users and purposes. Data intensive knowledge infrastructures tend to increase homogeneity and standardisation rather than complexity and diversity. They also tend to erase friction and elide omissions in data, and to foreground data as seamless, presenting themselves as maximally productive. In addition, current knowledge infrastructures focus on interoperability, automation and transparency and aim to organize data in ways that primarily enable computation (Peterson and Panofsky 2021), whereas we know that digital and data-intensive environments can also support uses other than algorithmic learning and classification, such as exploration or projection (Wouters and Beaulieu 2006; Wouters et al. 2013). These alternative approaches are important to cultivate. When values such as heterogeneity, diversity and recomposition come to the fore, data intensive resources can powerfully support the needs of a greater variety of users and concerns (Whitelaw and Smaill 2021; Beaulieu 2024), create new digital public spaces (Anderson 2013), and increase their potential in contributing to liveable futures.