_The Infrastructure of Accountability _brings together leading and emerging scholars who set forth an ambitious conceptual framework for understanding the full impact of large-scale, performance-based accountability systems on education. Over the past 20 years, schools and school systems have been utterly reshaped by the demands of test-based accountability. Interest in large-scale performance data has reached an unprecedented high point. Yet most education researchers focus primarily on questions of data quality and the effectiveness of data use. In this bold and thought-provoking (...) volume, the contributors look beneath the surface of all this activity to uncover the hidden infrastructure that supports the production, flow, and use of data in education, and explore the impact of these large-scale information systems on American schooling. These systems, the editors note, “sit at the juncture of technical networks, work practices, knowledge production, and moral order. (shrink)

The ArgumentQuantification is not merely a strategy for describing the social and natural worlds, but a means of reconfiguring them. It entails the imposition of new meanings and the disappearance of old ones. Often it is allied to systems of experimental or administrative control, and in fact considerable feats of human organization are generally required even to create stable, reasonably standardized measures. This essay urges that the uses of quantification in science, social science, and bureaucratic social and economic policy are (...) analogous in important ways to accountancy. (shrink)

Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science. Prospecting aims to render the data, knowledge, expertise, and practices of worldly domains available and tractable to data science method and epistemology. Prospecting precedes data synthesis, analysis, (...) or visualization, and is constituted by the upstream work of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for computation. Through this work, data science positions itself in the middle of all things—capable of engaging this, that, or any domain—and thus prospecting is a key driver of data science’s ongoing formation as a universal science. (shrink)

New data-driven ideas of healthcare have increased pressures to reform existing data infrastructures. This article explores the role of data governing institutions during a reform of both secondary health data infrastructure and related legislation in Finland. The analysis elaborates on recent conceptual work on data journeys and data frictions, connecting them to institutional and regulatory issues. The study employs an interpretative approach, using interview and document data. The results show the stark contrast between the goals of open and Big Data (...) inspired reforms and the existing institutional realities. The multiple tensions that emerged during the process indicate how data frictions emanate to the institutional level, and how mundane data practices and institutional dynamics are intertwined. The article argues that in the Finnish case, public institutions acted as sage-guards of public interest, preventing more controversial parts from passing. Finally, it argues that initiating regulatory and infrastructural reforms simultaneously was beneficial for solving the tensions of the initiative and analysing either side separately would have produced misleading accounts of the overall initiative. The results highlight the benefits of analysing institutional dynamics and data practices as connected issues. (shrink)