Introducing Regime Cluster Theory: Framing Regional Diffusion Dynamics of Democratization and Autocracy Promotion
Bibliographic record
Abstract
Recently the role of ideology and hegemony has received increased attention to explain varying dynamics of diffusion and autocratic cooperation. As a result, patterns of interaction in clusters from regions without hegemony or ideology have been overlooked because their autocracy-toautocracy transitions are no threat to the global status of democracy, even when active regime promotion is very common. This article will apply insights from economic cluster theory to political regimes and introduce a typology to differentiate among clusters. Regime Cluster Theory is the first framework that presents three ideal-types of ideological, hegemonic and biotopical regime clusters. With a new concept of ‘biotopical clusters’ the paper explains the dynamics of clusters in often omitted regions, like in Sub Saharan Africa, Latin America during the Cold War, or Central Asia during the 1990s. RCT offers a dynamic approach to recognize and assess patterns of forcible regime promotion per cluster as well as distinguish between their different diffusion patterns (coercive, voluntary, bounded learning, contagion) in four arenas: institutions, ideas, policy and administrative practices. RCT advances the comparative study of regime promotion and diffusion in various regions of the world and hopes to shed new light on related theories of alliance formation, regional institutionalization, and (conflict) spill-over effects.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".