Looting the Looters: The Paradox of Anti-Corruption Crusades in Nigeria’s Fourth Republic (1999-2014)
Bibliographic record
Abstract
The ubiquitous nature of corruption in Nigeria's political boulevard has been a cogged wheel that backpedal developmental liberation in the country. Consequently, Nigerian government embarked on major reform that led to the creation of Economic and Financial Crime Commission (EFCC) to eliminate the scars of corruption in Nigeria's political map during Olusegun Obasanjo's regime. However, the anti-corruption institution has received a lot of criticism among the general populace in Nigeria. The agency has been accused of politicization, selective operation, lack of transparency and as an instrument of political persecution. Therefore, this study investigates to what extent Economic and Financial Crime Commission (EFCC) becomes a political device in the hand of politicians in facilitating further looting and persecuting political enemies. The paper argues that various regimes in Nigeria's fourth republic, ranging from Olusegun Obasanjo's administration (1999-2007), Umaru Musa Yar’Adua’s regime (2007-2010) and Goodluck Jonathan’s political dispensation (2010-2014) employed anti-corruption agency as a shield to foster corruption and persecute political opposition. A drastic measure is suggested for the efficiency, effectiveness and absolute autonomy of the agency in Nigeria.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".