This book claims that what people really care about when they complain and protest that privacy has been violated is not the act of sharing information itself—most people understand that this is crucial to social life —but the inappropriate, improper sharing of information.Arguing that privacy concerns should not be limited solely to concern about control over personal information, Helen Nissenbaum counters that information ought to be distributed and protected according to norms governing distinct social contexts—whether it be workplace, health care, schools, or among family and friends. She warns that basic distinctions between public and private, informing many current privacy policies, in fact obscure more than they clarify. In truth, contemporary information systems should alarm us only when they function without regard for social norms and values, and thereby weaken the fabric of social life.

    Using actual unfinished, "working" notes as examples, they illustrate options for composing, reviewing, and working fieldnotes into finished texts. They discuss different organizational and descriptive strategies, including evocation of sensory detail, synthesis of complete scenes, the value of partial versus omniscient perspectives, and of first person versus third person accounts. Of particular interest is the author's discussion of notetaking as a mindset. They show how transforming direct observations into vivid descriptions results not simply from good memory but more crucially from learning to envision scenes as written. A good ethnographer, they demonstrate, must learn to remember dialogue and movement like an actor, to see colors and shapes like a painter, and to sense moods and rhythms like a poet.The authors also emphasize the ethnographer's core interest in presenting the perceptions and meanings which the people studied attach to their own actions. They demonstrate the subtle ways that writers can make the voices of people heard in the texts they produce. Finally, they analyze the "processing" of fieldnotes—the practice of coding notes to identify themes and methods for selecting and weaving together fieldnote excerpts to write a polished ethnography.This book, however, is more than a "how-to" manual. The authors examine writing fieldnotes as an interactive and interpretive process in which the researcher's own commitments and relationships with those in the field inevitably shape the character and content of those fieldnotes. They explore the conscious and unconscious writing choices that produce fieldnote accounts. And they show how the character and content of these fieldnotes inevitably influence the arguments and analyses the ethnographer can make in the final ethnographic tale.This book shows that note-taking is a craft that can be taught. Along with Tales of the Field and George Marcus and Michael Fisher's Anthropology as Cultural Criticism, Writing Ethnographic Fieldnotes is an essential tool for students and social scientists alike.

    Indeed, at a time when many academic institutions are facing austerity budgets, digital humanities programs have been able to hire new faculty, establish new centers and initiatives, and attract multimillion-dollar grants. Clearly the digital humanities has reached a significant moment in its brief history. But what sort of moment is it? Debates in the Digital Humanities brings together leading figures in the field to explore its theories, methods, and practices and to clarify its multiple possibilities and tensions. From defining what a digital humanist is and determining whether the field has (or needs) theoretical grounding, to discussions of coding as scholarship and trends in data-driven research, this cutting-edge volume delineates the current state of the digital humanities and envisions potential futures and challenges. At the same time, several essays aim pointed critiques at the field for its lack of attention to race, gender, class, and sexuality; the inadequate level of diversity among its practitioners; its absence of political commitment; and its preference for research over teaching. Together, the essays in Debates in the Digital Humanities—which will be published both as a printed book and later as an ongoing, open-access website—suggest that the digital humanities is uniquely positioned to contribute to the revival of the humanities and academic life. Contributors: Bryan Alexander, National Institute for Technology in Liberal Education; Rafael Alvarado, U of Virginia; Jamie “Skye” Bianco, U of Pittsburgh; Ian Bogost, Georgia Institute of Technology; Stephen Brier, CUNY Graduate Center; Daniel J. Cohen, George Mason U; Cathy N. Davidson, Duke U; Rebecca Frost Davis, National Institute for Technology in Liberal Education; Johanna Drucker, U of California, Los Angeles; Amy E. Earhart, Texas A&M U; Charlie Edwards; Kathleen Fitzpatrick, Pomona College; Julia Flanders, Brown U; Neil Fraistat, U of Maryland; Paul Fyfe, Florida State U; Michael Gavin, Rice U; David Greetham, CUNY Graduate Center; Jim Groom, U of Mary Washington; Gary Hall, Coventry U, UK; Mills Kelly, George Mason U; Matthew Kirschenbaum, U of Maryland; Alan Liu, U of California, Santa Barbara; Elizabeth Losh, U of California, San Diego; Lev Manovich, U of California, San Diego; Willard McCarty, King’s College London; Tara McPherson, U of Southern California; Bethany Nowviskie, U of Virginia; Trevor Owens, Library of Congress; William Pannapacker, Hope College; Dave Parry, U of Texas at Dallas; Stephen Ramsay, U of Nebraska, Lincoln; Alexander Reid, SUNY at Buffalo; Geoffrey Rockwell, Canadian Institute for Research Computing in the Arts; Mark L. Sample, George Mason U; Tom Scheinfeldt, George Mason U; Kathleen Marie Smith; Lisa Spiro, National Institute for Technology in Liberal Education; Patrik Svensson, Umeå U; Luke Waltzer, Baruch College; Matthew Wilkens, U of Notre Dame; George H. Williams, U of South Carolina Upstate; Michael Witmore, Folger Shakespeare Library.

    They gleefully whip the media into a frenzy over a fake teen drug crisis; they post offensive messages on Facebook memorial pages, traumatizing grief-stricken friends and family; they use unabashedly racist language and images. They take pleasure in ruining a complete stranger's day and find amusement in their victim's anguish. In short, trolling is the obstacle to a kinder, gentler Internet. To quote a famous Internet meme, trolling is why we can't have nice things online.Or at least that's what we have been led to believe. In this provocative book, Whitney Phillips argues that trolling, widely condemned as obscene and deviant, actually fits comfortably within the contemporary media landscape. Trolling may be obscene, but, Phillips argues, it isn't all that deviant. Trolls' actions are born of and fueled by culturally sanctioned impulses -- which are just as damaging as the trolls' most disruptive behaviors.Phillips describes, for example, the relationship between trolling and sensationalist corporate media -- pointing out that for trolls, exploitation is a leisure activity; for media, it's a business strategy. She shows how trolls, "the grimacing poster children for a socially networked world," align with social media. And she documents how trolls, in addition to parroting media tropes, also offer a grotesque pantomime of dominant cultural tropes, including gendered notions of dominance and success and an ideology of entitlement. We don't just have a trolling problem, Phillips argues; we have a culture problem. This Is Why We Can't Have Nice Things isn't only about trolls; it's about a culture in which trolls thrive.

    It's a book about the fate of one idea--that the Internet might liberate us forever from government, borders, and even our physical selves. We learn of Google's struggles with the French government and Yahoo's capitulation to the Chinese regime; of how the European Union sets privacy standards on the Net for the entire world; and of eBay's struggles with fraud and how it slowly learned to trust the FBI. In a decade of events the original vision is uprooted, as governments time and time again assert their power to direct the future of the Internet. The destiny of the Internet over the next decades, argue Goldsmith and Wu, will reflect the interests of powerful nations and the conflicts within and between them.While acknowledging the many attractions of the earliest visions of the Internet, the authors describe the new order, and speaking to both its surprising virtues and unavoidable vices. Far from destroying the Internet, the experience of the last decade has lead to a quiet rediscovery of some of the oldest functions and justifications for territorial government. While territorial governments have unavoidable problems, it has proven hard to replace what legitimacy governments have, and harder yet to replace the system of rule of law that controls the unchecked evils of anarchy. While the Net will change some of the ways that territorial states govern, it will not diminish the oldest and most fundamental roles of government and challenges of governance.Well written and filled with fascinating examples, including colorful portraits of many key players in Internet history, this is a work that is bound to stir heated debate in the cyberspace community.

    But who are these people? What is their world really like? Sharon Traweek, a bold and original observer of culture, opens the door to this unusual domain and offers us a glimpse into the inner sanctum.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    He believes that we should investigate what makes some countries more capable than others. Complex products—from films to robots, apps to automobiles—are a physical distillation of an economy’s knowledge, a measurable embodiment of its education, infrastructure, and capability. Economic wealth accrues when applications of this knowledge turn ideas into tangible products; the more complex its products, the more economic growth a country will experience.A radical new interpretation of global economics, Why Information Grows overturns traditional assumptions about the development of economies and the origins of wealth and takes a crucial step toward making economics less the dismal science and more the insightful one.

    The only reference text to identify the top resources in major subject areas and genres, it shows students how to approach the reference query by matching specific types of questions to the most appropriate format (when answering questions that require handy facts, for example, go first to ready reference sources; for questions about current events and issues, start with indexes). The book begins with the essentials -- interviewing patrons, determining the information need, and developing a basic search strategy. It then gives a thorough overview of the materials, print and electronic, most frequently used to answer questions -- from government information to bibliographic resources, dictionaries, encyclopedias, biographical information sources, atlases, and more. A section on special topics in reference includes chapters on when and how to use the Internet as a reference tool, suggestions on user instruction at the reference desk, and reader's advisory work, as well as a chapter on service to children and youth authored by acclaimed expert Mary K. Chelton. Finally, the book addresses reference management basics: selection and evaluation of material, management of the reference department, assessing and improving reference services, and future trends. Guided by an advisory board and a focus group, the authors have achieved an ideal balance between practical elements and guiding principles. This landmark text is sure to be of interest to LIS educators, students, and both novice and experienced reference professionals.

    Mumford has drawn on every aspect of life to explain the machine and to trace its social results. "An extraordinarily wide-ranging, sensitive, and provocative book about a subject upon whichphilosophers have so far shed but little light" (Journal of Philosophy).

    But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's "many-model paradigm," which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage.

    Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this bias, in time, money, and often with their lives.Celebrated feminist advocate Caroline Criado Perez investigates the shocking root cause of gender inequality and research in Invisible Women​, diving into women’s lives at home, the workplace, the public square, the doctor’s office, and more. Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.

    

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.