Studying Those Who Study Us

Diana E. Forsythe was a leading anthropologist of science, technology, and work who pioneered the field of the anthropology of artificial intelligence. This volume collects her best-known essays, along with other major works that remained unpublished upon her death in 1997. It is also an exemplar of how reflexive ethnography should be done.

A look at how to teach history in the age of easily accessible—but not always reliable—information. Let’s start with two truths about our era that are so inescapable as to have become clichés: We are surrounded by more readily available information than ever before. And a huge percent of it is inaccurate. Some of the bad info is well-meaning but ignorant. Some of it is deliberately deceptive. All of it is pernicious. With the Internet at our fingertips, what’s a teacher of history to do? In Why Learn History (When It’s Already on Your Phone), professor Sam Wineburg has the answers, beginning with this: We can’t stick to the same old read-the-chapter-answer-the-question snoozefest. If we want to educate citizens who can separate fact from fake, we have to equip them with new tools. Historical thinking, Wineburg shows, has nothing to do with the ability to memorize facts. Instead, it’s an orientation to the world that cultivates reasoned skepticism and counters our tendency to confirm our biases. Wineburg lays out a mine-filled landscape, but one that with care, attention, and awareness, we can learn to navigate. The future of the past may rest on our screens. But its fate rests in our hands. Praise for Why Learn History (When It’s Already on Your Phone) “If every K-12 teacher of history and social studies read just three chapters of this book—”Crazy for History,” “Changing History . . . One Classroom at a Time,” and “Why Google Can’t Save Us” —the ensuing transformation of our populace would save our democracy.” —James W. Lowen, author of Lies My Teacher Told Me and Teaching What Really Happened “A sobering and urgent report from the leading expert on how American history is taught in the nation’s schools. . . . A bracing, edifying, and vital book.” —Jill Lepore, New Yorker staff writer and author of These Truths “Wineburg is a true innovator who has thought more deeply about the relevance of history to the Internet—and vice versa—than any other scholar I know. Anyone interested in the uses and abuses of history today has a duty to read this book.” —Niall Ferguson, senior fellow, Hoover Institution, and author of The Ascent of Money and Civilization

A bold call to deromanticize education and reframe universities as terrains of struggle between alternative modes of studying and world-making Higher education is at an impasse. Black Lives Matter and #MeToo show that racism and sexism remain pervasive on campus, while student and faculty movements fight to reverse increased tuition, student debt, corporatization, and adjunctification. Commentators typically frame these issues as crises for an otherwise optimal mode of intellectual and professional development. In Beyond Education, Eli Meyerhoff instead sees this impasse as inherent to universities, as sites of intersecting political struggles over resources for studying. Meyerhoff argues that the predominant mode of study, education, is only one among many alternatives and that it must be deromanticized in order to recognize it as a colonial-capitalist institution. He traces how key elements of education—the vertical trajectory of individualized development, its role in preparing people to participate in governance through a pedagogical mode of accounting, and dichotomous figures of educational waste (the “dropout”) and value (the “graduate”)—emerged from histories of struggles in opposition to alternative modes of study bound up with different modes of world-making. Through interviews with participants in contemporary university struggles and embedded research with an anarchist free university, Beyond Education paves new avenues for achieving the aims of an “alter-university” movement to put novel modes of study into practice. Taking inspiration from Black Lives Matter, Occupy Wall Street, and Indigenous resurgence projects, it charts a new course for movements within, against, and beyond the university as we know it.

The paradoxical logic of transparency and mediation Transparency is the metaphor of our time. Whether in government or corporate governance, finance, technology, health or the media – it is ubiquitous today, and there is hardly a current debate that does not call for more transparency. But what does this word actually stand for and what are the consequences for the life of individuals? Can knowledge from the arts, and its play of visibility and invisibility, tell us something about the paradoxical logics of transparency and mediation? This Obscure Thing Called Transparency gathers contributions by international experts who critically assess the promises and perils of transparency today.

"Now with a new afterword by the author"--Back cover.

Considering studying history at university? Wondering whether a history degree will get you a good job, and what you might earn? Want to know what it’s actually like to study history at degree level? This book tells you what you need to know. Studying any subject at degree level is an investment in the future that involves significant cost. Now more than ever, students and their parents need to weigh up the potential benefits of university courses. That’s where the Why Study series comes in. This series of books, aimed at students, parents and teachers, explains in practical terms the range and scope of an academic subject at university level and where it can lead in terms of careers or further study. Each book sets out to enthuse the reader about its subject and answer the crucial questions that a college prospectus does not.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala