Uncertain Archives: Critical Keywords for Big Data
Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt
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Uncertain Archives : Critical Keywords for Big Data. / Thylstrup, Nanna ; Agostinho, Daniela; D'Ignazio, Catherine; Ring, Annie; Veel, Kristin.
Uncertain Archives: Critical Keywords for Big Data . red. / Nanna Bonde Thylstrup; Daniela Agostinho; Annie Ring; Catherine D'Ignazio; Kristin Veel. MIT Press, 2021. s. 1-27.Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt
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TY - CHAP
T1 - Uncertain Archives
T2 - Critical Keywords for Big Data
AU - Thylstrup, Nanna
AU - Agostinho, Daniela
AU - D'Ignazio, Catherine
AU - Ring, Annie
AU - Veel, Kristin
PY - 2021
Y1 - 2021
N2 - This groundbreaking work offers an interdisciplinary perspective on big data and the archives they accrue, interrogating key terms. Scholars from a range of disciplines analyze concepts relevant to critical studies of big data, arranged glossary style—from abuse and aggregate to visualization and vulnerability. They not only challenge conventional usage of such familiar terms as prediction and objectivity but also introduce such unfamiliar ones as overfitting and copynorm. The contributors include a broad range of leading and agenda-setting scholars, including as N. Katherine Hayles, Wendy Hui Kyong Chun, Johanna Drucker, Lisa Gitelman, Safiya Noble, Sarah T. Roberts and Nicole Starosielski.Uncertainty is inherent to archival practices; the archive as a site of knowledge is fraught with unknowns, errors, and vulnerabilities that are present, and perhaps even amplified, in big data regimes. Bringing lessons from the study of the archive to bear on big data, the contributors consider the broader implications of big data's large-scale determination of knowledge.
AB - This groundbreaking work offers an interdisciplinary perspective on big data and the archives they accrue, interrogating key terms. Scholars from a range of disciplines analyze concepts relevant to critical studies of big data, arranged glossary style—from abuse and aggregate to visualization and vulnerability. They not only challenge conventional usage of such familiar terms as prediction and objectivity but also introduce such unfamiliar ones as overfitting and copynorm. The contributors include a broad range of leading and agenda-setting scholars, including as N. Katherine Hayles, Wendy Hui Kyong Chun, Johanna Drucker, Lisa Gitelman, Safiya Noble, Sarah T. Roberts and Nicole Starosielski.Uncertainty is inherent to archival practices; the archive as a site of knowledge is fraught with unknowns, errors, and vulnerabilities that are present, and perhaps even amplified, in big data regimes. Bringing lessons from the study of the archive to bear on big data, the contributors consider the broader implications of big data's large-scale determination of knowledge.
UR - https://mitpress.mit.edu/books/uncertain-archives
M3 - Book chapter
SN - 9780262539883
SP - 1
EP - 27
BT - Uncertain Archives
A2 - Thylstrup, Nanna Bonde
A2 - Agostinho, Daniela
A2 - Ring, Annie
A2 - D'Ignazio, Catherine
A2 - Veel, Kristin
PB - MIT Press
ER -
ID: 300919623