[DATAGOV Core] [CPDP paper] case selection

[Stefania Milan]

See whether you like it. Intro and conclusion should also be slightly adjusted to pre-empt potential reviewer confusion

Cases

This article adopts a comparative case-based design to advance a conceptual argument about how regulatory data infrastructures produce infrastructural inequalities. The cases function as theory-building cases: they are used to identify, articulate, and compare recurrent socio-technical mechanisms through which inequality becomes embedded at the infrastructural level. The analytical goal is twofold. First, the article contributes to the development of the concept of regulatory data infrastructures by showing that these systems are not merely technical infrastructures but assemblages of institutional mandates, standards, and organisational arrangements. Second—and most importantly—it advances the notion of infrastructural inequalities by examining how such assemblages rework and stabilise existing structural inequalities as durable infrastructural conditions. Taken together, the cases allow us to observe these concepts dynamically in operation, as they unfold through concrete configurations of standards, interoperability, institutional mandates, and data practices.

Following established traditions in comparative qualitative research (e.g., Yin, 2014), as well as Critical Data Studies’ call for situated and context-sensitive analysis (e.g., Iliadis & Russo, 2016), we treat cases as analytical lenses rather than bounded empirical units. Each case is approached as a data assemblage—that is, as a configuration of legal mandates, institutional arrangements, data practices, etc.—through which governance is enacted and stabilised. Following Kitchin (2017, p. 21), we further conceptualise each case as “contingent, ontogenetic, and performative in nature”, where ontogenetic refers to the fact that data infrastructures are always “in a state of becoming”. This perspective foregrounds the dynamic and evolving character of RDIs, rather than treating them as fixed or fully stabilised systems. This design enables us to trace how shared infrastructural logics—such as interoperability, enrolment requirements, standardisation, and recursive data reuse—operate across different policy domains and geopolitical contexts.

The two cases—interoperable biometric border control infrastructures in the EU and health data infrastructures under India’s Ayushman Bharat Digital Mission—were selected through theoretical sampling. They represent contrasting regulatory domains (security/migration and healthcare) and distinct political-institutional settings (a supranational regulatory regime and a large federal state), while sharing key characteristics that make them analytically comparable as RDIs. In both cases, access to rights, services, or mobility is increasingly mediated through regulatory data infrastructures that operate as (quasi-)mandatory gateways and distribute accountability across multiple actors and layers.

This combination of difference and structural similarity allows us to examine how infrastructural inequalities emerge not from domain-specific policy choices alone, but from underlying infrastructural arrangements. Comparing the two cases makes it possible to distinguish between different modes of infrastructural inequality—what we conceptualise as hyper-legibility and uneven legibility—while identifying a shared set of inequality-producing mechanisms that travel across contexts. These include scope creep through interoperability, constrained opt-out and redress, standardisation around an “ideal” data subject, data poverty, and the recursive persistence of classifications over time.

It is worth noting that theswe cases are not presented as exhaustive or exceptional. They are strategic cases chosen because they foreground dynamics that are increasingly characteristic of governance by data infrastructure more broadly. By analysing RDIs at the level of infrastructure rather than individual systems or decision points, the comparison enables us to move beyond sector-specific accounts of algorithmic bias or digital exclusion and to understand how inequality becomes stabilised through infrastructural design choices.

In this sense, the purpose of the cases is conceptual elaboration and mechanism identification, rather than empirical coverage. The contribution of the article lies in specifying where inequality is produced (at the infrastructural level), how it is produced, reproduced, and sustained, and why it is durable across domains and jurisdictions. The cases serve as grounded sites through which these dynamics can be made analytically visible and comparable.


References to add:
Iliadis, A., & Russo, F. (2016). Critical data studies: An introduction. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716674238
Yin, R. K. (2014). Case Study Research: Design and Methods (5th ed.). Sage.
Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29.https://doi.org/10.1080/1369118X.2016.1154087
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