Data feminism / (Record no. 73639)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03975nam a2200517 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 9072233 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220712204950.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200505s2020 mau ob 001 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780262358521 |
| -- | electronic bk. |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic bk. |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 305.42 |
| 100 0# - AUTHOR NAME | |
| Author | Kanarinka, |
| 245 10 - TITLE STATEMENT | |
| Title | Data feminism / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (328 pages). |
| 490 1# - SERIES STATEMENT | |
| Series statement | Strong ideas |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction: 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Social aspects. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Methodology |
| -- | Social aspects. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Klein, Lauren F., |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=9072233 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge, Massachusetts : |
| -- | The MIT Press, |
| -- | [2020] |
| 264 #2 - | |
| -- | [Piscataqay, New Jersey] : |
| -- | IEEE Xplore, |
| -- | [2020] |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Feminism. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Feminism and science. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big data |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Quantitative research |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Power (Social sciences) |
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