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.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".