Diverse Democracies, Divergent Corruption: Examining the Impact of Democratic Governance Models in Curbing Corruption
Bibliographic record
Abstract
This study examined the impact of various democratic models on regime corruption. This study focuses on four types of democracy: liberal, deliberative, participatory, and egalitarian. Principal component regression was conducted on data from 183 countries spanning the period 1900–2022. The results show that liberal democracy significantly reduces regime corruption, suggesting that higher levels of liberal democratic values effectively curb it. The results indicate that there is no significant relationship between deliberative democracy and regime corruption, suggesting that deliberations do not directly influence corruption. Contrary to expectations, participatory democracy exhibited a significantly positive relationship with regime corruption, implying that corrupt actors might exploit vulnerabilities inherent in participatory mechanisms. Therefore, although participatory processes are essential for democratic engagement, they must be carefully designed and managed to prevent their misuse. On the other hand, egalitarian democracy shows a significantly negative relationship with corruption, emphasizing the importance of equal opportunities to curb corruption within democracies. These findings underscore the need to examine democratic governance from a more nuanced perspective. Liberal and egalitarian values are critical in developing effective anticorruption strategies.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".