Bibliographic record
Abstract
This article examines the conditions under which dictators hold competitive elections, and looks specifically at the role played by a dictator’s age. Drawing on previous studies arguing that uncertainty increases the likelihood of competitive elections, I argue that as a dictator ages, uncertainty over the future increases within the regime, because government insiders’ expected payoffs for supporting the incumbent decline as s/he ages. As a result, I argue that older dictators are more likely to hold competitive elections in order to reduce uncertainty. The article also tests an implication of the argument: if uncertainty over the future drives elections, then it should be mitigated in regimes with a clear successor. Using a large-N, cross-national dataset on autocrats and competitive elections between 1960 and 2012, this article examines the argument and finds that as dictators age, they are more likely to hold competitive elections, all else equal. The analysis also finds that the effect of autocrats’ age on competitive elections is mitigated in one-party regimes where there exists an established succession rule, while the effect is more apparent in personalist regimes without such a system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".