Fragmenting Dominant Coalitions Causing Political Violence: Rwanda and Ethiopia as Limited Access Orders
Bibliographic record
Abstract
Mainstream political and economic approaches can fall short when applied to unrest in developing countries. Political theories focus on formal arrangements while neglecting informal networks. These networks often are more important in determining political realities in developing societies. They also lack precise criteria of state strength or weakness Fearon and Laitin, 2003. In economic frameworks, a lack of economic opportunity, by state interference, leads to conflict Collier and Hoeffler, 2002. Theories of corruption, rent seeking, and clientelism predict breakdown of social institutions as a direct result of politics hindering market forces Krueger 1974, Hutchcroft 1997, Manzetti and Wilson 2007, Kaufmann 1997. What is missing is a mechanism linking arrangements of political and economic power to the risk of violence. North’s theory of limited access orders (LAO) can provide this framework North et al 2013. This paper will apply North’s LAO theory to two Sub-Saharan African countries, Ethiopia and Rwanda. Both countries exhibit extreme horizontal economic and political inequality due to ethnic domination by a minority. Yet, only in Ethiopia has horizontal inequality led to political instability. This paper finds that the LAO framework useful for identifying institutional factors causing violence. After applying the framework to Ethiopia and Rwanda, the paper concludes that recent political instability in Ethiopia was caused by contradictions between formal political institutions and informal arrangements of power. This contradiction creates opportunities for internal rent seeking. Rwanda has escaped this through formalizing its method of rent seeking and distribution. Discipline: Political Science Faculty Mentor: Dr. Andrea Wagner
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".