The Efficacy of Ethnic Stacking: Military Defection during Uprisings in Africa
Bibliographic record
Abstract
Abstract Does ethnic stacking in the armed forces help prevent military defection? Recent research, particularly in Africa and the Middle East, suggests so; by favoring in-groups, regimes can keep in-group soldiers loyal. In-group loyalty comes at the cost of antagonizing members of out-groups, but many regimes gladly run that risk. In this research note, we provide the first large-scale evidence on the impact of ethnic stacking on the incidence of military defection during uprisings from below, using data on fifty-seven popular uprisings in Africa since formal independence. We find clear evidence for the downside: ethnic stacking is associated with more frequent defection if out-group members are still dominant in the armed forces. We find more limited support for the hypothesized payoff. Ethnic stacking may reduce the risk of defection, but only in regimes without a recent history of coup attempts. Future research should therefore trace the solidification of ethnic stacking over time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".