The administrative reform of the datafied public administration places great emphasis on the classification, control, and prediction of citizen behavior and therefore has the potential to significantly impact citizen–state relations. There is a growing body of literature on data-oriented activism which aims to resist and counteract existing harmful data practices. However, little is known about the processes, policies, and political-economic structures that make datafication possible. There is a distinct research gap on situated and context-specific empirical research, which critically interrogates the (...) premises, interests, and agendas of data-driven public administration and how stakeholders can impact them. This paper therefore studies the conditions of participation in public administration datafication. It asks the overall research question of how citizens are problematized and included in policy and practitioner discourse in the datafication of public administration. The paper takes Norway as its case and applies Cardullo and Kitchin’s scaffold of smart citizen participation at the system level. It makes use of a unique empirical insight into the field, consisting of a survey, interviews, and an extensive document analysis. Unexpectedly, we find that citizens and civil society are rarely engaged in this administrative reform. Instead, we identify a paternalistic, top-down, technocratic approach where the context, values, and agendas of datafication are obscured from the citizen. (shrink)

The introduction of digital technologies in collective actions seems to have transformed the dynamics of movement organizing and enabled divergent forms of protest organizing. While some studies emphasize “organizationless” organizing in which traditional organizational forms—social movements organizations and formal-bureaucratic structures—have been pushed into the margins, other studies showcase how traditional forms have assumed alternative features, for example, connective leadership and organizations with fluid boundaries. While existing research correctly points out the evolving organizing dynamics and forms in digital activism, few studies (...) have accounted for why digitally enabled protests take certain organizing forms over others among multiple modes of interaction between protesters and digital technologies. Using a case study of a protest campaign organized by Chinese American immigrants, this study illustrates why immigrant activists struggled to keep the campaign “organizationless” on WeChat, a China-based digital platform that afforded other forms of organizing over such an organizing mode. Building on the mechanism-based approach in social movement studies, the findings show that immigrant activists’ emotional–cognitive responses to the changing digital environments became the driving force behind the relational choices to maintain the protest “organizationless.” The study, therefore, may not only inform future studies to explore why certain structures of protest networks emerge and develop but also contribute to the mechanism-based approach by foregrounding emotional–cognitive mechanisms, which mediate environmental and relational mechanisms. (shrink)

The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public data trusts and personal data sovereignty. We propose (...) a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe. (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)