| data science in politics: R for Political Data Science Francisco Urdinez, Andres Cruz, 2020-11-18 R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package, including all data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions. |
| data science in politics: Public Policy Analytics Ken Steif, 2021-08-18 Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government. |
| data science in politics: Data Analysis for Politics and Policy Edward R. Tufte, 1974 Introduction to data analysis; Predictions and projections: some issues of research design; Two-variable linear regression; Multiple regression. |
| data science in politics: Hacking the Electorate Eitan Hersh, 2015-06-09 Hacking the Electorate focuses on the consequences of campaigns using microtargeting databases to mobilize voters in elections. Eitan Hersh shows that most of what campaigns know about voters comes from a core set of public records, and the content of public records varies from state to state. This variation accounts for differences in campaign strategies and voter coalitions across the nation. |
| data science in politics: Data Politics Didier Bigo, Engin Isin, Evelyn Ruppert, 2019-03-13 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. |
| data science in politics: Introduction to Data Science for Social and Policy Research Jose Manuel Magallanes Reyes, 2017-09-21 This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R. |
| data science in politics: Retooling Politics Andreas Jungherr, Gonzalo Rivero Rodríguez, Gonzalo Rivero, Daniel Gayo-Avello, 2020-06-11 Provides academics, journalists, and general readers with bird's-eye view of data-driven practices and their impact in politics and media. |
| data science in politics: Big Data, Political Campaigning and the Law Normann Witzleb, Moira Paterson, Janice Richardson, 2019-12-06 In this multidisciplinary book, experts from around the globe examine how data-driven political campaigning works, what challenges it poses for personal privacy and democracy, and how emerging practices should be regulated. The rise of big data analytics in the political process has triggered official investigations in many countries around the world, and become the subject of broad and intense debate. Political parties increasingly rely on data analytics to profile the electorate and to target specific voter groups with individualised messages based on their demographic attributes. Political micro-targeting has become a major factor in modern campaigning, because of its potential to influence opinions, to mobilise supporters and to get out votes. The book explores the legal, philosophical and political dimensions of big data analytics in the electoral process. It demonstrates that the unregulated use of big personal data for political purposes not only infringes voters’ privacy rights, but also has the potential to jeopardise the future of the democratic process, and proposes reforms to address the key regulatory and ethical questions arising from the mining, use and storage of massive amounts of voter data. Providing an interdisciplinary assessment of the use and regulation of big data in the political process, this book will appeal to scholars from law, political science, political philosophy and media studies, policy makers and anyone who cares about democracy in the age of data-driven political campaigning. |
| data science in politics: Big Data and Democracy Macnish Kevin Macnish, 2020-06-18 What's wrong with targeted advertising in political campaigns? Should we be worried about echo chambers? How does data collection impact on trust in society? As decision-making becomes increasingly automated, how can decision-makers be held to account? This collection consider potential solutions to these challenges. It brings together original research on the philosophy of big data and democracy from leading international authors, with recent examples - including the 2016 Brexit Referendum, the Leveson Inquiry and the Edward Snowden leaks. And it asks whether an ethical compass is available or even feasible in an ever more digitised and monitored world. |
| data science in politics: Discriminating Data Wendy Hui Kyong Chun, 2021-11-02 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. |
| data science in politics: The Politics and Policies of Big Data Ann Rudinow Sætnan, Ingrid Schneider, Nicola Green, 2018 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. |
| data science in politics: Data Science for Fake News Deepak P, Tanmoy Chakraborty, Cheng Long, Santhosh Kumar G, 2021-04-29 This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools. The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news. The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research. |
| data science in politics: The SAGE Handbook of Research Methods in Political Science and International Relations Luigi Curini, Robert Franzese, 2020-04-09 The SAGE Handbook of Research Methods in Political Science and International Relations offers a comprehensive overview of research processes in social science — from the ideation and design of research projects, through the construction of theoretical arguments, to conceptualization, measurement, & data collection, and quantitative & qualitative empirical analysis — exposited through 65 major new contributions from leading international methodologists. Each chapter surveys, builds upon, and extends the modern state of the art in its area. Following through its six-part organization, undergraduate and graduate students, researchers and practicing academics will be guided through the design, methods, and analysis of issues in Political Science and International Relations: Part One: Formulating Good Research Questions & Designing Good Research Projects Part Two: Methods of Theoretical Argumentation Part Three: Conceptualization & Measurement Part Four: Large-Scale Data Collection & Representation Methods Part Five: Quantitative-Empirical Methods Part Six: Qualitative & Mixed Methods |
| data science in politics: Quantitative Social Science Kosuke Imai, Lori D. Bougher, 2021-03-16 Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a translation of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place-- |
| data science in politics: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data science in politics: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 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. |
| data science in politics: Prototype Politics Daniel Kreiss, 2016-06-01 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. |
| data science in politics: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science in politics: Deep Learning for NLP and Speech Recognition Uday Kamath, John Liu, James Whitaker, 2019-06-10 This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies. |
| data science in politics: Text as Data Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, 2022-03-29 A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry |
| data science in politics: Advances in Experimental Political Science James N. Druckman, Donald P. Green, 2021-04 Novel collection of essays addressing contemporary trends in political science, covering a broad array of methodological and substantive topics. |
| data science in politics: Rescuing Science from Politics Wendy Elizabeth Wagner, Rena Steinzor, 2006-07-24 This book examines how dominant interest groups manipulate the available science to support their positions. |
| data science in politics: Processing Politics Doris A. Graber, 2012-07-15 How often do we hear that Americans are so ignorant about politics that their civic competence is impaired, and that the media are to blame because they do a dismal job of informing the public? Processing Politics shows that average Americans are far smarter than the critics believe. Integrating a broad range of current research on how people learn (from political science, social psychology, communication, physiology, and artificial intelligence), Doris Graber shows that televised presentations—at their best—actually excel at transmitting information and facilitating learning. She critiques current political offerings in terms of their compatibility with our learning capacities and interests, and she considers the obstacles, both economic and political, that affect the content we receive on the air, on cable, or on the Internet. More and more people rely on information from television and the Internet to make important decisions. Processing Politics offers a sound, well-researched defense of these remarkably versatile media, and challenges us to make them work for us in our democracy. |
| data science in politics: Data-Driven Personalisation in Markets, Politics and Law Uta Kohl, Jacob Eisler, 2021-07-29 This book critiques the use of algorithms to pre-empt personal choices in its profound effect on markets, democracy and the rule of law. |
| data science in politics: Thinking Like a Political Scientist Christopher Howard, 2017-03-06 There are a plethora of books that aim to teach the research methods needed for political science. Thinking Like a Political Scientist stands out from them in its conviction that students are better served by learning a handful of core lessons well rather than trying to memorize hundreds of often statistical definitions. Short and concise, the book has two main parts, Asking Good Questions and Generating Good Answers. In the first section, one chapter each is devoted to the three fundamental questions in political science: who cares?, what happened?, and why?. These take up, among many other topics, crafting a literature review, creating hypotheses, measuring concepts, and the difference between correlation and causation. The second section of the book has chapters about choosing a research design, choosing cases, working with written documents, and working with numbers. All of these are essential skills for undergraduates to have when reading published work and conducting their own research. Every chapter ends with several exercises where students can read examples from published work and develop their own skills as researchers. Finally, unlike most research methods books, Christopher Howard s sprinkles humor and surprising analogies throughout. |
| data science in politics: Politics and Big Data Andrea Ceron, Luigi Curini, Stefano Maria Iacus, 2016-12-19 The importance of social media as a way to monitor an electoral campaign is well established. Day-by-day, hour-by-hour evaluation of the evolution of online ideas and opinion allows observers and scholars to monitor trends and momentum in public opinion well before traditional polls. However, there are difficulties in recording and analyzing often brief, unverified comments while the unequal age, gender, social and racial representation among social media users can produce inaccurate forecasts of final polls. Reviewing the different techniques employed using social media to nowcast and forecast elections, this book assesses its achievements and limitations while presenting a new technique of sentiment analysis to improve upon them. The authors carry out a meta-analysis of the existing literature to show the conditions under which social media-based electoral forecasts prove most accurate while new case studies from France, the United States and Italy demonstrate how much more accurate sentiment analysis can prove. |
| data science in politics: Kennedy and Roosevelt Michael Beschloss, 2016-08-16 The revealing story of Franklin Roosevelt, Joe Kennedy, and a political alliance that changed history, from a New York Times–bestselling author. When Franklin Roosevelt ran for president in 1932, he gained the support of Joseph Kennedy, a little-known businessman with Wall Street connections. Instrumental in Roosevelt’s victory, their partnership began a longstanding alliance between two of America’s most ambitious power brokers. Kennedy worked closely with FDR as the first chairman of the Securities and Exchange Commission, and later as ambassador to Great Britain. But at the outbreak of World War II, sensing a threat to his family and fortune, Kennedy lobbied against American intervention—putting him in direct conflict with Roosevelt’s intentions. Though he retreated from the spotlight to focus on the political careers of his sons, Kennedy’s relationship with Roosevelt would eventually come full circle in 1960, when Franklin Roosevelt Jr. campaigned for John F. Kennedy’s presidential win. With unprecedented access to Kennedy’s private diaries as well as firsthand interviews with Roosevelt’s family and White House aides, New York Times–bestselling author Michael Beschloss—called “the nation’s leading presidential historian” by Newsweek—presents an insightful study in contrasts. Roosevelt, the scion of a political dynasty, had a genius for the machinery of government; Kennedy, who built his own fortune, was a political outsider determined to build a dynasty of his own. From the author of The Conquerors and Presidential Courage, this is a “fascinating account of the complex, ambiguous relationship of two shrewd, ruthless, power-hungry men” (The New York Times Book Review). |
| data science in politics: Foundations of European Politics Catherine E. De Vries, Sara Binzer Hobolt, Sven-Oliver Proksch, Jonathan B. Slapin, 2021 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. |
| data science in politics: That Noble Science of Politics Stefan Collini, Donald Winch, John Burrow, 1983-11-24 In this work, three historians of ideas examine the forms taken in nineteenth-century Britain to develop a 'science of politics'. |
| data science in politics: Future-Proofing the Judiciary Brian Opeskin, 2022-01-01 This book reinvigorates the field of socio-legal inquiry examining the relationship between law and demography. Originally conceived as 'population law' in the 1960s following a growth in population and a use of law to temper population growth, this book takes a new approach by examining how population change can affect the legal system, rather than the converse. It analyses the impact of demographic change on the judicial system, with a geographic focus on Australian courts but with global insights and it raises questions about institutional structures. Through four case studies, it examines how demographic change impacts on the judicial system and how should the judicial system adapt to embody a greater preparedness for the demographic changes that lie ahead? It makes recommendations for reform and speaks to applied demographers, socio-legal scholars, and those interested in judicial institutions. |
| data science in politics: Computational Frameworks for Political and Social Research with Python Josh Cutler, Matt Dickenson, 2020-04-22 This book is intended to serve as the basis for a first course in Python programming for graduate students in political science and related fields. The book introduces core concepts of software development and computer science such as basic data structures (e.g. arrays, lists, dictionaries, trees, graphs), algorithms (e.g. sorting), and analysis of computational efficiency. It then demonstrates how to apply these concepts to the field of political science by working with structured and unstructured data, querying databases, and interacting with application programming interfaces (APIs). Students will learn how to collect, manipulate, and exploit large volumes of available data and apply them to political and social research questions. They will also learn best practices from the field of software development such as version control and object-oriented programming. Instructors will be supplied with in-class example code, suggested homework assignments (with solutions), and material for practical lab sessions. |
| data science in politics: Political Turbulence Helen Margetts, Peter John, Scott Hale, Taha Yasseri, 2017-09-05 How social media is giving rise to a chaotic new form of politics As people spend increasing proportions of their daily lives using social media, such as Twitter and Facebook, they are being invited to support myriad political causes by sharing, liking, endorsing, or downloading. Chain reactions caused by these tiny acts of participation form a growing part of collective action today, from neighborhood campaigns to global political movements. Political Turbulence reveals that, in fact, most attempts at collective action online do not succeed, but some give rise to huge mobilizations—even revolutions. Drawing on large-scale data generated from the Internet and real-world events, this book shows how mobilizations that succeed are unpredictable, unstable, and often unsustainable. To better understand this unruly new force in the political world, the authors use experiments that test how social media influence citizens deciding whether or not to participate. They show how different personality types react to social influences and identify which types of people are willing to participate at an early stage in a mobilization when there are few supporters or signals of viability. The authors argue that pluralism is the model of democracy that is emerging in the social media age—not the ordered, organized vision of early pluralists, but a chaotic, turbulent form of politics. This book demonstrates how data science and experimentation with social data can provide a methodological toolkit for understanding, shaping, and perhaps even predicting the outcomes of this democratic turbulence. |
| data science in politics: Between Politics and Science David H. Guston, 2000-01-13 Professor Guston provides an analysis of the changing relationship between politics and science in America. |
| data science in politics: The Politics of Pure Science Daniel S. Greenberg, 1999-08 Dispelling the myth of scientific purity and detachment, Daniel S. Greenberg documents in revealing detail the political processes that underpinned government funding of science from the 1940s to the 1970s. |
| data science in politics: The Timeline of Presidential Elections Robert S. Erikson, Christopher Wlezien, 2012-08-24 In presidential elections, do voters cast their ballots for the candidates whose platform and positions best match their own? Or is the race for president of the United States come down largely to who runs the most effective campaign? It’s a question those who study elections have been considering for years with no clear resolution. In The Timeline of Presidential Elections, Robert S. Erikson and Christopher Wlezien reveal for the first time how both factors come into play. Erikson and Wlezien have amassed data from close to two thousand national polls covering every presidential election from 1952 to 2008, allowing them to see how outcomes take shape over the course of an election year. Polls from the beginning of the year, they show, have virtually no predictive power. By mid-April, when the candidates have been identified and matched in pollsters’ trial heats, preferences have come into focus—and predicted the winner in eleven of the fifteen elections. But a similar process of forming favorites takes place in the last six months, during which voters’ intentions change only gradually, with particular events—including presidential debates—rarely resulting in dramatic change. Ultimately, Erikson and Wlezien show that it is through campaigns that voters are made aware of—or not made aware of—fundamental factors like candidates’ policy positions that determine which ticket will get their votes. In other words, fundamentals matter, but only because of campaigns. Timely and compelling, this book will force us to rethink our assumptions about presidential elections. |
| data science in politics: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science in politics: Modern Political Campaigns Michael D. Cohen, 2021-06-15 Modern Political Campaigns brings together academic, practical, and interviews to help understand how professionalism, technology, and speed have revolutionized elections, creating more voter-centric races for public office. Dr. Michael D. Cohen, a 20+ year veteran of working on, teaching, and writing about political campaigns take readers through how campaigns are organized, state-of-the-art tools of the trade, and how some of the most interesting people in politics got their big breaks. The book takes readers through clear-eyed chapters on parties and elections, campaign planning and management, fundraising, independent groups, vulnerability and opposition research, data and analytics, focus groups and polling, earned, paid and social media, and field operations. Finally, the book revisits the Permanent Campaign in terms of modern approaches to winning elections raising questions about today’s uniform preference for turnout over persuasion and what that means for our American democracy. Modern Political Campaigns will appeal to students and political activists interested in working in political campaigns. It is also a great read for anyone who wants to better understand the nuts and bolts of campaigns in practical terms from professionals, and the opportunities they provide all of us to be more engaged citizens and hold our leaders more accountable each Election Day. |
| data science in politics: Deep Roots Avidit Acharya, Matthew Blackwell, Maya Sen, 2020-03-10 Despite dramatic social transformations in the United States during the last 150 years, the South has remained staunchly conservative. Southerners are more likely to support Republican candidates, gun rights, and the death penalty, and southern whites harbor higher levels of racial resentment than whites in other parts of the country. Why haven't these sentiments evolved or changed? Deep Roots shows that the entrenched political and racial views of contemporary white southerners are a direct consequence of the region's slaveholding history, which continues to shape economic, political, and social spheres. Today, southern whites who live in areas once reliant on slavery--compared to areas that were not--are more racially hostile and less amenable to policies that could promote black progress. Highlighting the connection between historical institutions and contemporary political attitudes, the authors explore the period following the Civil War when elite whites in former bastions of slavery had political and economic incentives to encourage the development of anti-black laws and practices. Deep Roots shows that these forces created a local political culture steeped in racial prejudice, and that these viewpoints have been passed down over generations, from parents to children and via communities, through a process called behavioral path dependence. While legislation such as the Civil Rights Act and the Voting Rights Act made huge strides in increasing economic opportunity and reducing educational disparities, southern slavery has had a profound, lasting, and self-reinforcing influence on regional and national politics that can still be felt today. A groundbreaking look at the ways institutions of the past continue to sway attitudes of the present, Deep Roots demonstrates how social beliefs persist long after the formal policies that created those beliefs have been eradicated.--Jacket. |
| data science in politics: Field Research in Political Science Diana Kapiszewski, Lauren M. MacLean, Benjamin L. Read, 2015-03-19 This book explains how field research contributes value to political science by exploring scholars' experiences, detailing exemplary practices, and asserting key principles. |
| data science in politics: The Tragedy of Political Science David M. Ricci, 1984-01-01 This book is both a comprehensive review and a thoughtful critique of the development of political science as an academic discipline in this century. David Ricci eloquently describes the tragic dilemma of political science in America: when political scholars deal with politics in a scientific fashion, they reveal facts that contradict democratic expectations; when the same scholars seek to justify those expectations, their moral arguments carry little professional weight.--Jacket. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
