How the Corruption Quadruple Helix Affects BRIC: A Case Study of Corruption in Big Emerging Economies
Bibliographic record
Abstract
This study surveys the institutional conditions that produce corruption in BRIC (Brazil, Russia, India and China) nations. While this study focuses on BRIC as a case study in institutional corruption, it emphasizes the special role that each nation plays in the evolution of BRIC as a part of the global political economy. We utilize a helix structure as a means of expressing the intertwined, trans-dimensional aspects of corruption in BRIC among its various institutions. Our “quadruple helix model” reveals that the presence of a strong, meaningful alternative civil society is a significant fourth helix in several big emerging economies. This model demonstrates that data collected from multiple sources can effectively characterize the multi-dimensional systemic features of corruption if they are understood as institutional forces that evolve in sync with one another. This model demonstrates that, while the conventional wisdom that economic growth reduces corruption, bureaucratization and other institutional problems can increase corruption, especially when there are few conscious efforts to manage growth in relation to the evolution of civil society.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".