Bibliographic record
Abstract
We explore the role of ruling elites in autocratic regimes and provide an assessment of tools useful to clarify the structure of opaque political environments.We first showcase the importance of analyzing autocratic regimes as non-unitary actors by discussing extant work on nondemocracies in Sub-Saharan Africa and China, where the prevailing view of winner-take-all contests can be clearly rejected.We show how specific biographical information about powerful cadres helps shed light upon the composition of the inner circles that empower autocrats.We further provide an application of these methods to the Democratic People's Republic of Korea (DPRK), one of the most personalistic, opaque, and data-poor political regimes in the world today.Employing information from DPRK state media on participants at official state events, we are able to trace the evolution and consolidation of Supreme Leader Kim Jong Un around the transition period following the death of his father, Kim Jong Il.The internal factional divisions of the DPRK are explored during and after this transition.Final general considerations for the future study of the political economy of development are presented.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".