Hiển thị các bài đăng có nhãn Big Data. Hiển thị tất cả bài đăng
Hiển thị các bài đăng có nhãn Big Data. Hiển thị tất cả bài đăng

Thứ Bảy, 8 tháng 7, 2023

Big Data and the Welfare State: How the Information Revolution Threatens Social Solidarity PDF

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Big Data and the Welfare State: How the Information Revolution Threatens Social Solidarity PDF

A core principle of the welfare state is that everyone pays taxes or contributions in exchange for universal insurance against social risks such as sickness, old age, unemployment, and plain bad luck. This solidarity principle assumes that everyone is a member of a single national insurance pool, and it is commonly explained by poor and asymmetric information, which undermines markets and creates the perception that we are all in the same boat. Living in the midst of an information revolution, this is no longer a satisfactory approach. This book explores, theoretically and empirically, the consequences of ‘big data’ for the politics of social protection. Torben Iversen and Philipp Rehm argue that more and better data polarize preferences over public insurance and often segment social insurance into smaller, more homogenous, and less redistributive pools, using cases studies of health and unemployment insurance and statistical analyses of life insurance, credit markets, and public opinion.




The Trouble With Big Data: How Datafication Displaces Cultural Practices PDF

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The Trouble With Big Data: How Datafication Displaces Cultural Practices PDF

  • Length: 192 pages
  • Edition: 1
  • Publisher: 
  • Publication Date: 2022-01-27

This book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.

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