Using Specialised Anti-Corruption Agencies to Combat Pervasive Corruption in Nigeria: A Critical Review of the ICPC and EFCC
Bibliographic record
Abstract
Abstract The use of specialised anti-corruption agencies (ACAs) to combat corruption is increasingly popular among African countries. This is no surprise considering the successes these agencies have recorded elsewhere in the world, on the strength of which they have been described as ‘the most innovative feature of the anti-corruption movement of the last two decades’. Yet while ACAs have been successful in other parts of the world, the same cannot be said of those in Africa generally and Nigeria in particular. Even with two ACAs – the Independent Corrupt Practices and Other Related Offences Commission (ICPC) and Economic and Financial Crimes Commission (EFCC) – corruption continues to soar in the country, making it necessary to examine the flaws of Nigeria’s ACAs. Focusing on a number of key characteristics of ACAs, this article analyses the role of the ICPC and EFCC in combating corruption in Nigeria. The main question the article seeks to answer is why corruption should be on the increase despite the fact that two specialised ACAs have been in existence for close to two decades.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".