Replication Data for: Unconditional Loyalty: The Survival of Minority Autocracies
Bibliographic record
Abstract
Contrary to the conventional view that minority regimes are vulnerable to breakdown, many of these regimes exhibit remarkable durability. From 1900 to 2015, minority autocracies that exclude a single majority ethnic group (e.g., regimes in Bahrain, Syria, and Apartheid South Africa) remained in power twice as long as other autocracies. This paper argues that this durability is rooted in their unique ethno-political configuration, which enables them to foster a largely unconditional loyalty due to the ruling minority's fear of being subjected to majoritarian rule. Such loyalty endows them with an exceptional capacity to withstand major challenges by fostering in-group demobilization and policing, pro-regime countermobilization, and coethnic elite loyalty. The article employs a multi-method approach, using a novel data set of minority regimes and a case study of Bahrain based on original interviews. The findings highlight the conditions under which ethnic group loyalty can play a central role in autocratic survival.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.047 |
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".