Evaluating Anti-Graft Agencies Governance Practices in Nigeria
Bibliographic record
Abstract
The Nigerian state has witnessed exponential increase in corruption and various anti-graft agencies have been established by government to curb this malaise which has branded the Nigerian state and its citizens all over the world as corrupt. The agencies have over the years been criticized as not being effective and a militia of government in power in the way they prosecute anti-corruption wars. The study evaluated the anti-graft agencies and their governance practices, their effectiveness in addressing the cankerworm in the country by employing the purposive sampling technique where 400 copies of questionnaires were distributed to professional accountants, bankers, journalist and lawyers. The data collected were analyzed using both descriptive and inferential statistics. The study revealed inadequate capacity of the workforce, non-commitment to integrity, ethical values and the rule of law, lack of openness, lack of transparency and accountability. The study recommends amendment of the extant laws establishing the anti-graft agencies for operational efficiency in prosecution, responsiveness to the constitution and adequate and targeted training for officials.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".