Under what conditions do women become more resilient and empowered in post conflict settings? Utilizing data from multi site fieldwork in northern Iraq, Germany, and the U.S. involving over 160 in depth interviews, this dissertation addresses this question by studying the experiences of Yezidi women who were subject to genocidal violence by the Islamic State in Iraq in 2014. By adopting an intersectional approach, it contributes to feminist research on post conflict dynamics and suggests that how women cope with trauma and achieve positive changes in their lives depends on a variety of factors. Age, history of sexual violence and displacement and immigration experiences of Yezidi female survivors as well as the intersection of these factors, emerge as main determinants of their post genocide resilience and empowerment. Older women and widowed women, especially when they have no educational or work background, show less resilience and are not likely to experience empowerment post conflict. While survivors of sexual violence and abduction undergo high levels of traumatic stress, they can also show the highest levels of post traumatic growth when they are supported by their families and communities. Moreover, since they have greater access to sources compared t o the rest of the community, they are also more likely to experience empowerment. Displacement is mostly a disempowering experience for survivors. In contrast, immigration may bring positive changes, depending on the conditions of immigration, host country politics, community support in settled places and individual background. In conclusion, the dissertation questions generalized assumptions about women's post conflict experiences as well as the established categories of victimhood and calls for a more effective and inclusive policymaking for women in post atrocity settings.
Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1827 |
Date | 01 January 2021 |
Creators | Ayhan Ergin, Tutku |
Publisher | STARS |
Source Sets | University of Central Florida |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Electronic Theses and Dissertations, 2020- |
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