Bibliographic record
Abstract
Invigorated by President Muhammadu Buhari administration's zero tolerance to corruption, the two major anti-graft agencies-the Economic and Financial Crimes Commission (EFCC) and the Independent Corrupt Practices and other Related Offences Commission (ICPC)-have stepped up investigation and prosecution of corrupt current and former public officers.There is palpable fear and concern everywhere in Nigeria now, especially among public treasury looters because there is certainly no hiding place for them as the war against corruption rages.Many who hitherto had been treated as untouchables have either been quizzed, arrested or facing prosecution in courts.The anti-graft agencies' dragnet has so far caught many.Indeed, it is judgement day for yesterday's men and women of impunity who made corruption a way of life.Perhaps, the most mind-boggling case is the one involving Sambo Dasuki, immediate past National Security Adviser (NSA) who is currently standing trial for allegedly mismanaging $2.1billion meant for arms procurement.It has opened a Pandora's Box which has tainted many serving and former military personnel and public servants as well as top politicians.Dasukigate, as the arms scandal is now known, is just one of the grand scams the anti-corruption agencies are tracking.Inspired by the President's body language, the EFCC's Chairman, Ibrahim Magu, has vowed that more influential Nigerians on the commission's radar would soon be arrested to face prosecution in the reinvigorated anti-corruption crackdown.President Muhammadu Buhari re-echoed recently that the high level of indiscipline and corruption in government and other social places are the main reasons he has pledged to fight graft.This paper examined the list of who is who in EFCC's net, provided insights into the antecedents of some of the personalities under probe, the allegations against them and their prosecution.Also on the list are other alleged treasure looters who are still under the EFCC's radar and would likely be apprehended and prosecuted after preliminary investigation by the anti-graft agency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.973 | 0.979 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".