Bibliographic record
Abstract
The control of corruption is an important element of governance and a stated goal of many governments. Legislative oversight is an acknowledged mechanism for controlling corruption, but little research has been undertaken on this subject, and national anticorruption strategies generally ignore the legislature. This article addresses the question of whether legislative oversight helps to curb perceived corruption, and if so, how. Statistical analysis using data from a global survey of 82 legislatures found that the presence of legislative oversight tools explains a major proportion of the variance in perceived levels of wrongdoing in countries with presidential forms of government, but much less in countries with semipresidential governments and less yet again in parliamentary systems. The analysis also found that the effectiveness of legislative oversight instruments varies by form of government. In presidential countries the most important instruments are committee hearings, hearings in plenary, and ombuds offices; in semipresidential countries they are question time, interpellations, and ombuds, while in parliamentary systems interpellations are the most important. These findings demonstrate that while the oversight apparatus is useful in helping curb malfeasance, not all tools are equally effective, and what works for some forms of government may not work as well, or at all, in other systems. Clearly, the "one size fits all" approach is inadequate when it comes to legislative corruption control.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".