This article provides new evidence of the ways that smart cities materialize within specific sites and contexts through smart street furniture (SSF). Drawing on empirical data generated through mixed-method field research, the article examines the situated data relations that emerge in the context of the adoption of InLinkUK smart kiosks in Glasgow and Strawberry Energy smart benches in London. The concept of “silences” is proposed to analyze insufficiently articulated data relations resulting from gaps or absences in the use, design, and governance of this new type of urban furniture. The argument made is that data silences lead to failures to account for decisions and the deferral of responsibilities regarding the data aspects of these objects. It is suggested that an approach that focuses on “listening” to and “speaking” about data relations can enable dialogical forms of accountability, and realize the potential of SSF for citizens in local contexts.

Little is known about how to develop frameworks for using AI to support learning in English Literature within higher education. This paper presents research that developed a framework for employing AI in the study of the fourteenth-century English poet Geoffrey Chaucer. Studying Chaucer presents challenges for modern readers, as it requires an understanding of his literary style, spoken Middle English, and the cultural and social context of the late medieval period. The research investigated how AI tools, such as ChatGPT and Amazon Polly, could support the study of Chaucer. It found that while AI can assist with students’ initial familiarisation with Chaucer’s poetry, it lacks the depth and rigour necessary for more advanced study. As a result, students and lecturers still rely on traditional resources, including authoritative print-based and digital scholarly editions. Consequently, the research developed a framework for studying Chaucer's poetry that integrates the guided use of AI through active learning with additional supporting resources, such as digital scholarly editions.

This paper discusses a methodology that seeks to address one of the challenges in working with a range of data in mixed-methods audience research, which is how to sort, order and categorise different data so that they can be systematically combined and interrogated. The methodology was developed as part of the “Beyond the Multiplex: audiences for specialised films in English regions” (BtM) project. This project required a mixed methods approach using surveys, interviews, focus groups and document analysis to explore the richness of audience experiences and trends in the context of regional film policy. This required a mixed methods approach using surveys, interviews, focus groups and document analysis. The project utilised a data model approach that uses the principles of a computational ontology in order to sort, order and categorise data for systematic interrogation. The paper discusses methods, data, coding, and the use of a data model to support data analysis. We argue that this approach enables the cross referencing of data that provides a rich, multi-layered and relational understanding of film audiences but requires time and attention to data management and coding. Although, additionally it also forms the basis of an open access data resource for future research.

This special issue was inspired by an Economic & Social Research Council funded seminar series that explored the possibilities for using Big Data and data analytics for assessing health and wellbeing risks within organisations. The aim of this special issue was to build on some of the themes developed in the seminar series and draw together and update some key insights from different disciplinary perspectives on the opportunities, challenges and lessons that could be applied in this area. This editorial, therefore, draws together the findings and themes from the submitted papers and interprets these in light of the findings from the seminar series.