Records of the Ministry of Labour PDF Download
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Author: Great Britain. Public Record Office
Publisher:
ISBN:
Category : Archives
Languages : en
Pages : 5
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Book Description
Author: Great Britain. Public Record Office
Publisher:
ISBN:
Category : Archives
Languages : en
Pages : 5
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Book Description
Author: United States. Employment Standards Administration. Wage and Hour Division
Publisher:
ISBN:
Category : Government publications
Languages : en
Pages : 20
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Book Description
Author: United States. Department of Labor. Wage and Hour Division
Publisher:
ISBN:
Category : Labor laws and legislation
Languages : en
Pages : 32
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Book Description
Author: International Labour Office
Publisher:
ISBN:
Category : Labor
Languages : en
Pages : 542
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Book Description
Author: Labour Research Department
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: United States. Dept. of Labor. Wage and Hour Division
Publisher:
ISBN:
Category :
Languages : en
Pages : 36
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Book Description
Author:
Publisher:
ISBN:
Category : Labor supply
Languages : en
Pages : 60
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Author: International Labour Office
Publisher: International Labour Organization
ISBN: 9789221103295
Category : Business & Economics
Languages : en
Pages : 62
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Book Description
An ILO code of practice
Author:
Publisher:
ISBN:
Category : Labor
Languages : en
Pages : 540
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Book Description
Contains references, abstracts, and a subject index to articles in Social and labour bulletin.
Author: Catherine D'Ignazio
Publisher: MIT Press
ISBN: 026254718X
Category : Social Science
Languages : en
Pages : 328
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Book Description
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.
