K
Kristen A. Harkness
Researcher at University of St Andrews
Publications - 11
Citations - 242
Kristen A. Harkness is an academic researcher from University of St Andrews. The author has contributed to research in topics: Democratization & Politics. The author has an hindex of 7, co-authored 10 publications receiving 187 citations.
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The Ethnic Army and the State Explaining Coup Traps and the Difficulties of Democratization in Africa
TL;DR: In Africa, military coups have posed a persistent threat to political stability in Africa, undermining democratization efforts, igniting insurgencies, and leading to years of devastating military governance as mentioned in this paper.
Book
When Soldiers Rebel: Ethnic Armies and Political Instability in Africa
TL;DR: The authors examines the causes of military coups in post-independence Africa and looks at the relationship between ethnic armies and political instability in the region, focusing on rebellions to protect rather than change the status quo.
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Military loyalty and the failure of democratization in Africa: how ethnic armies shape the capacity of presidents to defy term limits
TL;DR: The authors conducted a mixed methods analysis of presidential bids to challenge term limits, including a paired comparison of Senegal and Cameroon, and demonstrated that ethnic armies triple the chances of success and, in so doing, encourage defiance in the first place.
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Military maladaptation: counterinsurgency and the politics of failure
TL;DR: In this paper, the British Army's counterinsurgency in the Southern Cameroons (1960-61) showed that despite meeting all preconditions thought to enable adaptation (decentralization, leadership turnover, supportive leadership, poor organizational memory, feedback loops, and a clear threat), the British still failed to adapt.
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Measuring the landscape of civil war: Evaluating geographic coding decisions with historic data from the Mau Mau rebellion
TL;DR: This work develops a systematic approach to georeferencing that articulates the strategies available, empirically diagnoses problems of bias created by both the data generating process and researcher-controlled tasks, and provides new generalizable tools for simultaneously optimizing both the recovery and accuracy of coordinates.