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A new examination of mass digitization as an emerging sociopolitical and sociotechnical phenomenon that alters the politics of cultural memory. Today, all of us with internet connections can access millions of digitized cultural artifacts from the comfort of our desks. Institutions and individuals add thousands of new cultural works to the digital sphere every day, creating new central nexuses of knowledge. How does this affect us politically and culturally? In this book, Nanna Bonde Thylstrup approaches mass digitization as an emerging sociopolitical and sociotechnical phenomenon, offering a new understanding of a defining concept of our time. Arguing that digitization has become a global c...
A new examination of mass digitization as an emerging sociopolitical and sociotechnical phenomenon that alters the politics of cultural memory. Today, all of us with internet connections can access millions of digitized cultural artifacts from the comfort of our desks. Institutions and individuals add thousands of new cultural works to the digital sphere every day, creating new central nexuses of knowledge. How does this affect us politically and culturally? In this book, Nanna Bonde Thylstrup approaches mass digitization as an emerging sociopolitical and sociotechnical phenomenon, offering a new understanding of a defining concept of our time. Arguing that digitization has become a global c...
Scholars from a range of disciplines interrogate terms relevant to critical studies of big data, from abuse and aggregate to visualization and vulnerability. This pathbreaking work offers an interdisciplinary perspective on big data, interrogating key terms. Scholars from a range of disciplines interrogate concepts relevant to critical studies of big data--arranged glossary style, from from abuse and aggregate to visualization and vulnerability--both challenging conventional usage of such often-used terms as prediction and objectivity and introducing such unfamiliar ones as overfitting and copynorm. The contributors include both leading researchers, including N. Katherine Hayles, Johanna Drucker and Lisa Gitelman, and such emerging agenda-setting scholars as Safiya Noble, Sarah T. Roberts and Nicole Starosielski.
Citizens of networked societies are almost incessantly accompanied by ecologies of images. These ecologies of still and moving images present a paradox of uncertainties emerging along with certainties. Images appear more certain as the technical capacities that render them visible increase. At the same time, images are touched by more uncertainty as their numbers, manipulabilities, and contingencies multiply. With the emergence of big data, the image is becoming a dominant vehicle for the construction and presentation of the truth of data. Images present themselves as so many promises of the certainty, predictability, and intelligibility offered by data. The focus of this book is twofold. It...
This Handbook explores the largely unchartered territory of media, technology, and organization studies, and interrogates their foundational relations, their forms, and their consequences. The chapters consider how specific mediating technological objects such as the Clock or the Smartphone help us to create organizational form.
A collection of essays exploring the intersection of dating and digital reality. Data Dating is a collection of eleven academic essays accompanied by eleven works of media art that provide a comprehensive insight into the construction of love and its practices in the time of digitally mediated relationships. The essays come from recognized researchers in the field of media and cultural studies.
How EU data practices establish and assign people to categories, and how this matters in enacting--"making up"--Europe as a population and people. What is "Europe" and who are "Europeans"? Data Practices approaches this contemporary political and theoretical question by treating it as a practical problem of counting. Only through the myriad data practices that make up methods such as censuses can EU member states know their national populations, and this in turn is utilized by the EU to understand the population of Europe. But this volume approaches data practices not simply as reflecting populations but as performative in two senses: they simultaneously enact--that is, "make up"--a European...
An archive is a collection of documents and records that is preserved for historical purposes. As such, an archive is considered a site of the past, a place that contains traces of a collective memory of a nation, a people or a group. Digital archives have changed from stable entities into flexible systems, referred to with the term ?Living Archives?. But in which ways has this change affected our relationship to the past, present and future? Will the erased, forgotten and neglected be redeemed, and new memories be allowed? Will the fictional versus factual mode of archiving offer the democracy that the public domain implies, or is it another way for public instruments of power to operate? 'Lost and Living (in) Archives' shows that an archive is not simply a recording, a reflection, or an image of an event, but that it shapes the event itself and thus influences both present and past.
How those with the power to design technology, in the very moment of design, are allowed to imagine who is included--and who is excluded--in the future. Our world is built on an array of standards we are compelled to share. In Proxies, Dylan Mulvin examines how we arrive at those standards, asking, "To whom and to what do we delegate the power to stand in for the world?" Mulvin shows how those with the power to design technology, in the very moment of design, are allowed to imagine who is included--and who is excluded--in the future. For designers of technology, some bits of the world end up standing in for other bits, standards with which they build and calibrate. These "proxies" carry spec...
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 lear...