Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Filtering by
- All Subjects: Women
Examining Twitter tweets and hashtags, the study explored how the discourse on women driving had been executed, particularly in between genders. The study analyzed a sizeable number of tweets as well as their context via linguistic corpora analysis. Following Norman Fairclough’s framework, the two opposing perspectives were investigated both at a level of textual analysis. The selected tweets were representative of the three hashtags that emerged on the heat of the discourse regarding the issue of women driving in Saudi Arabia: #Women_car_driving, #I_will_drive_my_car_June15, and #I_will_enter_my_kitchen_June15.
The results showed, among others, that tweets with the hashtag #Women_car_driving presented a tremendous support towards the movement. On the other hand strong opposing reactions emerged from the hashtags #I_will_drive_my_car_June15 and #I_will_enter_my_kitchen_June15.
Significant health inequalities exist between different castes and ethnic communities in India, and identifying the roots of these inequalities is of interest to public health research and policy. Research on caste-based health inequalities in India has historically focused on general, government-defined categories, such as “Scheduled Castes,” “Scheduled Tribes,” and “Other Backward Classes.” This method obscures the diversity of experiences, indicators of well-being, and health outcomes between castes, tribes, and other communities in the “scheduled” category. This study analyzes data on 699,686 women from 4,260 castes, tribes and communities in the 2015-2016 Demographic and Health Survey of India to: (1) examine the diversity within and overlap between general, government-defined community categories in both wealth, infant mortality, and education, and (2) analyze how infant mortality is related to community category membership and socioeconomic status (measured using highest level of education and household wealth). While there are significant differences between general, government-defined community categories (e.g., scheduled caste, backward class) in both wealth and infant mortality, the vast majority of variation between communities occurs within these categories. Moreover, when other socioeconomic factors like wealth and education are taken into account, the difference between general, government-defined categories reduces or disappears. These findings suggest that focusing on measures of education and wealth at the household level, rather than general caste categories, may more accurately target those individuals and households most at risk for poor health outcomes. Further research is needed to explain the mechanisms by which discrimination affects health in these populations, and to identify sources of resilience, which may inform more effective policies.