Work

Counting absence, accounting care: Digital thresholds and the classification of absence in schools

Year: 2026

Type: article

Abstract: Data-driven classifications and categorisations have long-shaped public sector practices in welfare states. Yet in the context of digital platforms and algorithmic systems, it is crucial to examine how socio-technical judgements are not only made through data but also how data themselves are made, structured by platform values and institutional logics. Drawing on ethnographic research in Swedish compulsory schooling, we argue that data in welfare institutions participate in governing through both care and control, a dynamic particularly evident in education where mundane indicators like school attendance are used to classify students as at-risk categories such as ‘persistent absentees’. The article unpacks the social ordering involved in such classifications, highlighting the complexities of what appears to be straightforward, data-driven decisions. Building on Fourcade's work on classificatory judgements, we examine how persistent absenteeism is produced as a socio-technical category through which institutions act. We show how care can function as control, and control may be exercised in caring ways, raising questions about whose needs are rendered visible, who is made vulnerable, and who is left out. By examining everyday data practices, we foreground the importance of critically engaging with how categories are made actionable, and how such practices carry consequences for justice, inclusion and the meaning of welfare in datafied educational contexts.

Source: Big Data & Society

DOI: 10.1177/20539517261498676

Authors: Svea Kiesewetter, Annika Bergviken Rensfeldt, Thomas Hillman

Institution: University of Gothenburg

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Quality education

Open Access status: gold

Estimated APC paid: $2,000