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210223s2020 mauab ob 001 0 eng d |
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|a 9780262358521
|q (electrónico)
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|a 0262358522
|q (electrónico)
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|a 9780262044004
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|a 0262044005
|q (impreso)
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|a (OCoLC)1238154788
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|a (OCoLC)on1238154788
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|a U@S
|b spa
|c U@S
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|a U@SA
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|a HQ1190
|b .K455 2020eb
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| 099 |
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|a Recurso electrónico en INTERNET
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| 100 |
1 |
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|a D'Ignazio, Catherine.
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| 245 |
1 |
0 |
|a Data feminism
|h [recurso electrónico] /
|c Catherine D'Ignazio and Lauren F. Klein.
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| 260 |
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|a Cambridge, Mass. :
|b The MIT Press,
|c c2020.
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| 300 |
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|a 1 recurso en línea (xii, 314 p.) :
|b il.
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| 490 |
1 |
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|a <strong> ideas series
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| 516 |
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|a Libro electrónico.
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| 500 |
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|a Título tomado de la pantalla de presentación (visto 23 de febrero de 2021)
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| 538 |
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|a Modo de acceso: Disponible en línea a través de Internet.
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| 504 |
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|a Incluye referencias bibliográficas e índice.
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| 505 |
0 |
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|a 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.
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| 520 |
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|a "A 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." --Descripción del editor.
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| 650 |
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0 |
|a Feminism.
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| 650 |
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0 |
|a Feminism and science.
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| 650 |
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0 |
|a Big data
|x Social aspects.
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| 650 |
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0 |
|a Quantitative research
|x Methodology
|x Social aspects.
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| 650 |
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0 |
|a Power (Social sciences)
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| 650 |
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7 |
|a Feminismo.
|2 UDESA
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| 650 |
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7 |
|a Feminismo y ciencia.
|2 UDESA
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| 650 |
|
7 |
|a Grandes volúmenes de datos
|x Aspectos sociales.
|2 UDESA
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| 650 |
|
7 |
|a Investigación cuantitativa
|x Metodología
|x Aspectos sociales.
|2 UDESA
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| 650 |
|
7 |
|a Poder (Ciencias sociales)
|2 UDESA
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| 700 |
1 |
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|a Klein, Lauren F.
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| 830 |
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0 |
|a ideas series
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| 856 |
4 |
0 |
|z Solo para usuarios autorizados, sin límite de accesos simultáneos
|u https://ebookcentral.proquest.com/lib/sanandres/detail.action?docID=6120950
|