Tackling Corruption in the Arab World, with Special Reference to Kuwait
Bibliographic record
Abstract
The present study highlights the effects of corruption in the Arab world and of a culture of corruption. It compares types of corruption and their implications for governance. The study analyzes the concept of corruption in the light of literature by various authors. It explains corruption in the Arab states and totalitarian oppression; moreover, it points out the reasons for corruption in these countries and attempts to redefine corruption, such as that of Fawaz Trabulsi. In particular, it emphasizes the case of Kuwait, which typifies the issue of corruption in the Arab states at large. The authors explain the causes of corruption in Kuwait and the role of the Kuwaiti parliament in combatting corruption, with a brief discussion of a survey conducted in Kuwait. As the study concludes, corruption ripples increasingly through political, psychological, moral, and family life, not only leading to financial bankruptcy but also increasing poverty and creating social and mental problems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".