The covid-19 pandemic forged a more intensely digital world, complicating civil society actors’ menu of options for channeling and framing their advocacy goals. As both a product and study of pandemic-era politics, this dissertation is concerned with understanding how the internet and social media shape associational life in Kazakhstan. I draw on three forms of ethnographic data collected online between October 2020 and February 2022, including semi-structured interviews, visual analysis of social media posts, and digital participant observation.
I demonstrate how Kazakhstani civil society actors devise strategies to pursue reform, how they debate theories of political change, and how they exercise agency in a political system that seeks to control the public sphere. I argue that civil society groups use social media platforms to leverage power differentials across levels of administration to advance rights claims and negotiate for reform. Activists and rights defenders flock to various social media platforms because of each site’s unique technological infrastructure. They leverage different logics of visibility and bridge physical and digital forms of contentious politics to demand accountability from an authoritarian government.
In addition to providing a more complete understanding of civil society dynamics in Kazakhstan, this study suggests that, in repressive contexts, civil society actors who opt for within-system engagement have not necessarily been coopted and activists do not always take dissent underground. This dissertation is an example of digital political ethnography, which stands to grow not only as a standalone method, but also a bridge to big data analysis in political science. I demonstrate the importance of an ethnographic sensibility while approaching the internet as a site of inquiry to understand political subjectivity.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/ty13-na49 |
| Date | January 2022 |
| Creators | Wood, Colleen |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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