Geopolitics of State Capture: Systemic Corruption as a Professional Service
Bibliographic record
Abstract
This chapter explores the geopolitical implications of systemic corruption by way of state capture. No longer a mere byproduct of weak governance, illicit gains from state capture have become an instrument of geopolitical leverage that large authoritarian states such as Russia and China use to project power and influence. State capture characterises a system where private interests collude to shape laws, policies, and regulations for their personal benefit by subverting public institutions. State capture endures due to systemic failures in domestic and international legal regimes and is exacerbated by economic globalisation, which enables transnational illicit financial flows associated with state capture. Case studies of South Africa under Jacob Zuma and the Eastern European Laundromat illustrate how financial institutions, corporate service providers, and public relations firms broker illicit influence. These professional enablers exploit legal asymmetries and regulatory gaps to launder wealth, obscure ownership, and legitimise corrupt regimes. The chapter reframes state capture as a political modality: a dynamic, transnational instrument of geopolitical competition that capitalises on an architecture of global finance and governance whose inadequate and inconsistent regulatory frameworks, notably FATF’s gatekeeper model, foster rampant non-compliance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".