Bibliographic record
Abstract
We investigate how nondemocratic regimes use the military and how this can lead to the emergence of military dictatorships.Nondemocratic regimes need the use of force in order to remain in power, but this creates a political moral hazard problem; a strong military may not simply work as an agent of the elite but may turn against them in order to create a regime more in line with their own objectives.The political moral hazard problem increases the cost of using repression in nondemocratic regimes and in particular, necessitates high wages and policy concessions to the military.When these concessions are not sufficient, the military can take action against a nondemocratic regime in order to create its own dictatorship.A more important consequence of the presence of a strong military is that once transition to democracy takes place, the military poses a coup threat against the nascent democratic regime until it is reformed.The anticipation that the military will be reformed in the future acts as an additional motivation for the military to undertake coups against democratic governments.We show that greater inequality makes the use of the military in nondemocratic regimes more likely and also makes it more difficult for democracies to prevent military coups.In addition, greater inequality also makes it more likely that nondemocratic regimes are unable to solve the political moral hazard problem and thus creates another channel for the emergence of military dictatorships.We also show that greater natural resource rents make military coups against democracies more likely, but have ambiguous effects on the political equilibrium in nondemocracies (because with abundant natural resources, repression becomes more valuable to the elite, but also more expensive to maintain because of the more severe political moral hazard that natural resources induce).Finally, we discuss how the national defense role of the military interacts with its involvement in domestic politics.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".