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Preventing Coups d’état
Abstract
This chapter discusses how the decision to prioritize coup prevention is a rational one. It explains that the threat of a coup is more immediate and unpredictable than the threat posed by civil war or international conflict. It describes a range of coup-proofing strategies that rulers adopt to hinder military effectiveness, reinforce ethnic and political divides, and drain financial resources. The chapter shows that counterbalancing the military with coercive institutions outside the regular military chain of command increases the risk that coup attempts will fail. It highlights that understanding how counterbalancing works can help predict where coup attempts will occur, whether they will succeed, and how violent they are likely to be. It points out that coups are the most common way dictatorships begin and end, and that even failed coup attempts can have deleterious effects. The chapter also talks about how rulers design their coercive institutions, and discusses how counterweights might help insulate rulers from successful coups, but weakens the state in a number of ways. It then states that the way rulers organize their coercive institutions affects the survival of their regimes. Finally, it discusses how coup plotters begin their efforts to seize power by recruiting potential supporters, and the potential costs to the perpetrators if the coup fails.
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In the citing paper
“Erica De Bruin ' s (2020) analysis of coup-proo fi ng shows howscope can be appropriately de fi ned for LNQA purposes by considering the tail of a distribution. It also illustrates how LNQA can focus on rare treatments.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as De Bruin (2020)
In the citing paper
“Put most simply, her hypothesis is that the more counterweights, the less likely that coups will be successful.”
From Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research (2023) · cites this paper as De Bruin (2020)
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Cited by
Large-N Qualitative Analysis (LNQA): Causal Generalization in Case Study and Multimethod Research
Goertz, G., & Haggard, S. (2023) · Perspectives on Politics · doi:10.1017/s1537592723002037 · Cited by 22
Metadata
- Authors
- De Bruin, E.
- Venue
- How to Prevent Coups d'État
- Publisher
- Cornell University Press
- Published
- 2020-11-15
- Pages
- 1-12
- Cited by
- 0
- References
- 0
- Language
- en
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.