The Sage Handbook of Data and Society
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A critical, interdisciplinary exploration of the social, political and cultural consequences of big data, computation, data technologies and ‘datafication’.
This is not a handbook of data science, but an overview of the social and political implications, everyday effects, and unexpected impacts of our increasingly datafied lives.
The Sage Handbook of Data and Society provides a comprehensive exploration of the impact of data on society. Addressing urgent research questions in this rapidly evolving field and offering a balanced mix of introductory insights and advanced analyses, this resource offers a nuanced understanding of critical data studies and their relevance to contemporary society. Through detailed examinations of specific issues, cases, concepts, and methodologies, the handbook fosters a critical proximity to the entanglement of social dynamics and their data doubles.
Organized into seven sections, the handbook covers a diverse range of topics, including data infrastructures, digital labor, power dynamics, environmental challenges, bodily experiences, scientific methodologies, and disciplinary intersections. Each section offers a broad examination of critical issues, highlighting interdisciplinary scholarship and presenting fresh perspectives on the intricate relationship between data and societal structures.
Featuring contributions from scholars representing various disciplines and global perspectives, The Sage Handbook of Data and Society is an invaluable resource for scholars, researchers, students, and practitioners. By encouraging critical engagement with the profound impacts of datafication, this handbook equips readers with the necessary tools to navigate the complexities of the digital age and comprehend its implications for contemporary society.
Section 1: Infrastructures of Data
Section 2: Labor of Data
Section 3: Power and Struggles of Data
Section 4: Data and Crises of Nature
Section 5: Data and Bodies
Section 6: The Sciences of Data
Section 7: Disciplines of Data
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