000 | 03975nam a2200517 i 4500 | ||
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001 | 9072233 | ||
003 | IEEE | ||
005 | 20220712204950.0 | ||
006 | m o d | ||
007 | cr |n||||||||| | ||
008 | 200505s2020 mau ob 001 eng d | ||
020 |
_a9780262358521 _qelectronic bk. |
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020 |
_z0262358522 _qelectronic bk. |
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020 | _z9780262044004 | ||
035 | _a(CaBNVSL)mat09072233 | ||
035 | _a(IDAMS)0b0000648c95d0fe | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aHQ1190 _b.K375 2020eb |
|
082 | 0 | 4 |
_a305.42 _223 |
100 | 0 |
_aKanarinka, _eauthor. _925889 |
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245 | 1 | 0 |
_aData feminism / _cCatherine D'Ignazio and Lauren F. Klein. |
264 | 1 |
_aCambridge, Massachusetts : _bThe MIT Press, _c[2020] |
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264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2020] |
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300 | _a1 PDF (328 pages). | ||
336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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490 | 1 | _aStrong ideas | |
505 | 0 | _aIntroduction: Why data science needs feminism -- Examine power : the power chapter -- Challenge power : collect, analyze, imagine, teach -- Elevate emotion and embodiment : on rational, scientific, objective viewpoints from mythical, imaginary, impossible standpoints -- Rethink binaries and hierarchies : "What gets counted counts" -- Embrace pluralism : unicorns, janitors, ninjas, wizards and rock stars -- Consider context : the numbers don't speak for themselves -- Make labor visible : show your work -- Conclusion: Now let's multiply. | |
506 | _aRestricted to subscribers or individual electronic text purchasers. | ||
520 | _aA new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom Data science for whom Data science with whose interests in mind The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics--one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever "speak for themselves." Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. | ||
530 | _aAlso available in print. | ||
538 | _aMode of access: World Wide Web | ||
650 | 0 |
_aFeminism. _925890 |
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650 | 0 |
_aFeminism and science. _925891 |
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650 | 0 |
_aBig data _xSocial aspects. _925892 |
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650 | 0 |
_aQuantitative research _xMethodology _xSocial aspects. _925893 |
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650 | 0 |
_aPower (Social sciences) _925894 |
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655 | 4 |
_aElectronic books. _93294 |
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700 | 1 |
_aKlein, Lauren F., _eauthor. _925895 |
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710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _925896 |
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710 | 2 |
_aMIT Press, _epublisher. _925897 |
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830 | 0 |
_a<strong> ideas series. _925898 |
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856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=9072233 |
942 | _cEBK | ||
999 |
_c73639 _d73639 |