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Available copies

  • 1 of 1 copy available at Sage Library System. (Show)
  • 1 of 1 copy available at Columbia Gorge Community College. (Show)
  • 1 of 1 copy available at Columbia Gorge Community College Library.

Current holds

0 current holds with 1 total copy.

Summary:

"We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data scientists seeking to learn how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is addressed to professionals in all fields where data-driven decisions are being made, as well as to communities that want to better understand the data that surrounds them. It is written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to better communicate the significance of such charts and statistics to others. This is an example-driven book written with a broad audience of scholars, students, and practitioners in mind. It offers a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by the ideas associated with intersectional feminist thought"--
Location Call Number / Copy Notes Barcode Shelving Location Circulation Modifier Age Hold Protection Active/Create Date Status Due Date
Columbia Gorge Community College Library 305.42 D'LGNA 2020 (Text) 39705000052711 New Book Shelf Book None 06/16/2021 Available -

Record details

  • ISBN: 9780262044004
  • ISBN: 0262044005
  • Physical Description: xii, 314 pages : illustrations (some color) ; 24 cm.
  • Publisher: Cambridge, Massachusetts : The MIT Press, [2020]

Content descriptions

Bibliography, etc. Note:
Includes bibliographical references and index.
Formatted Contents Note:
Introduction : Why data science needs feminism -- The power chapter -- Collect, analyze, imagine, teach -- On rational, scientific, objective viewpoints from mythical, imaginary, impossible standpoints -- "What gets counted counts" -- Unicorns, janitors, ninja, wizards, and rock stars -- The numbers don't speak for themselves -- Show your work -- Conclusion : now let's multiply.
Summary, etc.:
"We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data scientists seeking to learn how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is addressed to professionals in all fields where data-driven decisions are being made, as well as to communities that want to better understand the data that surrounds them. It is written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to better communicate the significance of such charts and statistics to others. This is an example-driven book written with a broad audience of scholars, students, and practitioners in mind. It offers a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by the ideas associated with intersectional feminist thought"-- Provided by publisher.
Subject: Feminism.
Feminism and science.
Big data > Social aspects.
Quantitative research > Methodology > Social aspects.
Power (Social sciences)
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264 1. ‡aCambridge, Massachusetts : ‡bThe MIT Press, ‡c[2020]
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520 . ‡a"We have seen through many examples that data science and artificial intelligence can reinforce structural inequalities like sexism and racism. Data is power, and that power is distributed unequally. This book offers a vision for a feminist data science that can challenge power and work towards justice. This book takes a stand against a world that benefits some (including the authors, two white women) at the expense of others. It seeks to provide concrete steps for data scientists seeking to learn how feminism can help them work towards justice, and for feminists seeking to learn how their own work can carry over to the growing field of data science. It is addressed to professionals in all fields where data-driven decisions are being made, as well as to communities that want to better understand the data that surrounds them. It is written for everyone who seeks to better understand the charts and statistics that they encounter in their day-to-day lives, and for everyone who seeks to better communicate the significance of such charts and statistics to others. This is an example-driven book written with a broad audience of scholars, students, and practitioners in mind. It offers a way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by the ideas associated with intersectional feminist thought"-- ‡cProvided by publisher.
50500. ‡tIntroduction : Why data science needs feminism -- ‡tThe power chapter -- ‡tCollect, analyze, imagine, teach -- ‡tOn rational, scientific, objective viewpoints from mythical, imaginary, impossible standpoints -- ‡t"What gets counted counts" -- ‡tUnicorns, janitors, ninja, wizards, and rock stars -- ‡tThe numbers don't speak for themselves -- ‡tShow your work -- ‡tConclusion : now let's multiply.
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650 0. ‡aFeminism and science. ‡0(DLC)sh 99002551
650 0. ‡aBig data ‡xSocial aspects.
650 0. ‡aQuantitative research ‡xMethodology ‡xSocial aspects.
650 0. ‡aPower (Social sciences) ‡0(DLC)sh 85105976
7001 . ‡aKlein, Lauren F., ‡eauthor.
830 0. ‡aideas series.
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