Zusammenfassungen
A guide to everything you need to understand to navigate a world increasingly governed by data. Data has become a defining issue of current times. Our everyday lives are shaped by the data that is produced about us (and by us) through digital technologies. In this book, Critical Data Literacies, Luci Pangrazio and Neil Selwyn introduce readers to the central concepts, ideas, and arguments required to make sense of life in the data age. The authors challenge the idea that datafication is an inevitable and inescapable condition. Drawing on emerging areas of scholarship such as data justice, data feminism, and other critical data studies approaches, they explore how individuals and communities can empower themselves to engage with data critically and creatively. Over the course of eight wide-ranging chapters, the book introduces readers to the main components of critical data literacies—from the fundamentals of identifying and understanding data to the complexities of engaging with more combative data tactics. Critical Data Literacies explores how the tradition of critical literacies can offer a powerful foundation to address the big concerns of the data age, such as issues of data justice and privacy, algorithmic bias, dataveillance, and disinformation. Bringing together cutting-edge thinking and discussion from across education, sociology, psychology, and media and communication studies, Critical Data Literacies develops a powerful argument for collectively rethinking the role that data plays in our everyday lives and re-establishing agency, free will, and the democratic public sphere.
Von Klappentext im Buch Critical Data Literacies (2023) Dieses Buch erwähnt ...
Personen KB IB clear | Richard Barbrook , Nick Barrowman , Catherine D'Ignazio , Virginia Eubanks , Kyle M. L. Jones , Rob Kitchin , Lauren F. Klein , Chase McCoy , Evgeny Morozov , Safiya Umoja Noble , Frank Pasquale , Thomas Poell , Jathan Sadowski , José van Dijck , Martijn de Waal , Shoshana Zuboff | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Aussagen KB IB clear | Es gibt keine neutralen Daten
Machine Learning kann bestehende Vorurteile/Ungerechtigkeiten verstärken/weitertragen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Begriffe KB IB clear | algorithmic bias , Algorithmusalgorithm , amazon , Amazons-KI-Bewerbungsverfahren , Bildungeducation (Bildung) , datafication , Datendata , Desinformationdisinformation , Digitalisierung , Informatikcomputer science , Informationinformation , Privatsphäreprivacy , Psychologiepsychology , Technologietechnology , Willensfreiheitfree will | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Dieses Buch erwähnt vermutlich nicht ...
Nicht erwähnte Begriffe | datafication in education, Datenschutz, Informatik-Didaktik, Informatik-Unterricht (Fachinformatik), LehrerIn, Lernen, Schule, Unterricht |
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Beat und dieses Buch
Beat hat dieses Buch während seiner Zeit am Institut für Medien und Schule (IMS) ins Biblionetz aufgenommen. Beat besitzt kein physisches, aber ein digitales Exemplar. (das er aber aus Urheberrechtsgründen nicht einfach weitergeben darf). Es gibt bisher nur wenige Objekte im Biblionetz, die dieses Werk zitieren.