Download Free Data Politics Book in PDF and EPUB Free Download. You can read online Data Politics and write the review.

Data has become a social and political issue because of its capacity to reconfigure relationships between states, subjects, and citizens. This book explores how data has acquired such an important capacity and examines how critical interventions in its uses in both theory and practice are possible. Data and politics are now inseparable: data is not only shaping our social relations, preferences and life chances but our very democracies. Expert international contributors consider political questions about data and the ways it provokes subjects to govern themselves by making rights claims. Concerned with the things (infrastructures of servers, devices, and cables) and language (code, programming, and algorithms) that make up cyberspace, this book demonstrates that without understanding these conditions of possibility it is impossible to intervene in or to shape data politics. Aimed at academics and postgraduate students interested in political aspects of data, this volume will also be of interest to experts in the fields of internet studies, international studies, Big Data, digital social sciences and humanities. The Open Access version of this book, available at https://www.routledge.com/Data-Politics-Worlds-Subjects-Rights/Bigo-Isin-Ruppert/p/book/9781138053267, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
Introduction to data analysis; Predictions and projections: some issues of research design; Two-variable linear regression; Multiple regression.
How big data and machine learning encode discrimination and create agitated clusters of comforting rage. In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data.
"Big Data, gathered together and re-analysed, can be used to form endless variations of our persons - so-called ‘data doubles’. Whilst never a precise portrayal of who we are, they unarguably contain glimpses of details about us that, when deployed into various routines (such as management, policing and advertising) can affect us in many ways.How are we to deal with Big Data? When is it beneficial to us? When is it harmful? How might we regulate it? Offering careful and critical analyses, this timely volume aims to broaden well-informed, unprejudiced discourse, focusing on: the tenets of Big Data, the politics of governance and regulation; and Big Data practices, performance and resistance.An interdisciplinary volume, The Politics of Big Data will appeal to undergraduate and postgraduate students, as well as postdoctoral and senior researchers interested in fields such as Technology, Politics and Surveillance."--Provided by publisher.
Given the advanced state of digital technology and social media, one would think that the Democratic and Republican Parties would be reasonably well-matched in terms of their technology uptake and sophistication. But as past presidential campaigns have shown, this is not the case. So what explains this odd disparity? Political scientists have shown that Republicans effectively used the strategy of party building and networking to gain campaign and electoral advantage throughout the twentieth century. In Prototype Politics, Daniel Kreiss argues that contemporary campaigning has entered a new technology-intensive era that the Democratic Party has engaged to not only gain traction against the Republicans, but to shape the new electoral context and define what electoral participation means in the twenty-first century. Prototype Politics provides an analytical framework for understanding why and how campaigns are newly "technology-intensive," and why digital media, data, and analytics are at the forefront of contemporary electoral dynamics. The book discusses the importance of infrastructure, the contexts within which technological innovation happens, and how the collective making of prototypes shapes parties and their technological futures. Drawing on an analysis of the careers of 629 presidential campaign staffers from 2004-2012, as well as interviews with party elites on both sides of the aisle, Prototype Politics details how and why the Democrats invested more in technology, were able to attract staffers with specialized expertise to work in electoral politics, and founded an array of firms to diffuse technological innovations down ballot and across election cycles. Taken together, this book shows how the differences between the major party campaigns on display in 2012 were shaped by their institutional histories since 2004, as well as that of their extended network of allied organizations. In the process, this book argues that scholars need to understand how technological development around politics happens in time and how the dynamics on display during presidential cycles are the outcome of longer processes.
This book critiques the use of algorithms to pre-empt personal choices in its profound effect on markets, democracy and the rule of law.
It humanizes high-level debates over indicators and data in development aid, showing how they are used to make life-or-death decisions.
Foundations of European Politics: A Comparative Approach offers an accessible introduction to European politics using a coherent comparative and analytical framework. It presents students with the basic theoretical and empirical toolkit of social scientific researchers, and explains how ananalytic approach can be used to understand both domestic and EU-level policy-making in Europe.The book draws on cutting edge research from all areas of European politics - from national and EU institutions, to political behaviour and policy-making - and uses case studies and examples throughout to help students compare different electoral systems, parties and governments across Europe.The book is structured thematically in five parts, beginning with theoretical foundations; moving on to examine citizens and voters, elections and parties, governments and policy; and finally covering the rule of law, democracy and backsliding.Digital formats and resourcesFoundations of European Politics: A Comparative Approach is available for students and institutions to purchase in a variety of formats, and is supported by online resources.DT The e-book offers a mobile experience and convenient access along with functionality tools, navigation features and links that offer extra learning support: www.oxfordtextbooks.co.uk/ebooks http://www.oxfordtextbooks.co.uk/ebooksDT Online resources for students include: multiple choice questions, web links, essay questions, and data descriptions and data exercises.DT Online resources for lecturers include: adaptable PowerPoint slides, test bank questions, figures and tables from the book.
Provides academics, journalists, and general readers with bird's-eye view of data-driven practices and their impact in politics and media.
A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.