Bibliographic record
Abstract
This dissertation examines the relationship between ethnic identity and the survival of authoritarian regimes in ethnically divided societies. Contrary to the conventional view that minority regimes are vulnerable to breakdown, many of these regimes exhibit remarkable durability. My dissertation provides the first systematic attempt to theorize and empirically examine minority autocracies. I find that minority regimes that exclude a single majority ethnic group exhibit exceptional durability and immunity from outsider anti-regime challenges. Prominent examples of such autocracies are the Togolese regime, which has been in power since 1963, the RPF regime in Rwanda which has been in power since 1994, and the Apartheid South African regime. On average, these regimes have remained in power more than twice as long as other autocracies. I argue that the durability of this type of minority autocracies is rooted in their unique ethno-political configuration, which allows them to foster a largely unconditional loyalty due to the ruling minority's fear of being subjected to majoritarian rule. Such heightened threat perception, in turn, leads to three dynamics that strengthen the regime: (1) cohesion among coethnic elites, enabling the regime to deploy the military in repression without fear of defections; (2) the demobilization of the ruler’s ethnic group, which becomes actively involved in policing and sanctioning deviant or dissenting coethnics; and (3) coethnic counter-mobilization, which involves the mobilization of civilian supporters and militias from the ruler's ethnic group who actively participate in repression. I test these arguments using a mixed-method approach that combines statistical analysis with in-depth case studies. My quantitative analysis draws on a novel dataset of all minority regimes from 1900 to 2015. Additionally, I conduct a small-N comparative study of three key cases: the current regime in Bahrain, the Assad regime in Syria, and the regime of Omar al-Bashir in Sudan. This qualitative analysis is based on in-person interviews conducted in Sudan, Bahrain, Lebanon, and London.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".