Image | ![]() |
EAN-13 | 9780812221626 ![]() |
Product Name | Gender Stereotyping: Transnational Legal Perspectives (Pennsylvania Studies In Human Rights) |
Language | English |
Category | Book / Magazine / Publication |
Short Description | Paperback |
Amazon.com | ![]() |
SKU | ACOUK_BOOK_USEDVERYGOOD_0812221621 |
Price New | 19.94 US Dollars (curriencies) |
Price Used | 14.68 US Dollars (curriencies) |
Width | 0.7 inches (convert) |
Height | 8.9 inches (convert) |
Length | 6 inches (convert) |
Weight | 14.72 ounces (convert) |
Author | Rebecca J. Cook, Simone Cusack |
Page Count | 288 |
Binding | Paperback |
Published | 01/07/2011 |
Features | Used Book in Good Condition |
Long Description | Drawing on domestic and international law, as well as on judgments given by courts and human rights treaty bodies, Gender Stereotyping offers perspectives on ways gender stereotypes might be eliminated through the transnational legal process in order to ensure women's equality and the full exercise of their human rights. A leading international framework for debates on the subject of stereotypes, the Convention on the Elimination of All Forms of Discrimination against Women, was adopted in 1979 by the UN General Assembly and defines what constitutes discrimination against women. It also establishes an agenda to eliminate discrimination in all its forms in order to ensure substantive equality for women. Applying the Convention as the primary framework for analysis, this book provides essential strategies for eradicating gender stereotyping. Its proposed methodology requires naming operative gender stereotypes, identifying how they violate the human rights of women, and articulating states' obligations to eliminate and remedy these violations. According to Rebecca J. Cook and Simone Cusack, in order to abolish all forms of discrimination against women, priority needs to be given to the elimination of gender stereotypes. While stereotypes affect both men and women, they can have particularly egregious effects on women, often devaluing them and assigning them to subservient roles in society. As the legal perspectives offered in Gender Stereotyping demonstrate, treating women according to restrictive generalizations instead of their individual needs, abilities, and circumstances denies women their human rights and fundamental freedoms. |
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Created | 11-16-2012 12:06:02pm |
Modified | 05-01-2020 2:53:07am |
MD5 | 8258fc45bce0f3fb2379b88c63a760ab |
SHA256 | d7934357ae5ccb162d6e235b2869cb1f6a49377e43b24e461d15835052084a5c |
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Article of interest
The attributes are somewhat like fields. They are the individual data items that describe each product. Each product entry will have several attributes. There is no telling which attributes will be attached to each product but they all have the same basic format.
Here is an example of one attribute. When accessing the data feed API, you can get your data in XML or JSON format. Here it is displayed in XML format to make it a little easier to read through.
<attribute> <field_name>product</field_name> <group_name>Over View</group_name> <title>Product Name</title> <data_type>varchar</data_type> <data_type_description>short text</data_type_description> <has_linked_text>0</has_linked_text> <has_linked_extra>0</has_linked_extra> </attribute>
In the above example you will find these elements:
- field_name - The unique name used to access this attribute.
- group_name - The name of the data group this field belongs to.
- title - The label we place on the screen when displaying this attribute to users.
- data_type - The database data type we are using to store this attribute.
- data_type_description - More infomation about the data type.
- has_linked_text - Some numeric fields have a text representation. We store the numeric value but if there is linked text, we use a lookup table to display that text instead of the number to the user.
- has_linked_extra - Some numeric fields have an extra text value that goes along with the number. We use a lookup table to display that text in addition to the number.
Hopefully, this helps you understand the data attributes you find in the data feed API.
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