Bibliographic record
Abstract
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 non‐democracies 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".