Treasury Single Account Policy and Revenue Generation Among Federal Parastatals in Ekiti-State, Nigeria
Bibliographic record
Abstract
Treasury Single Account (TSA) is a new government policy to fight corruption in the Nigerian public sector. The policy has received a lot of compliments from many Nigerians; however, empirical studies have produced mixed results. The study examined the effects of Treasury Single Accounts (TSA) on the revenue generation of federal government parastatals in Ekiti state. The study specifically examined the effect of TSA on revenue generation of Federal University Oye-Ekiti, Federal Teaching Hospital Ido, Federal Road Safety Commission Ado-Ekiti and Federal Polytechnics Ado-Ekiti. Both Descriptive and inferential statistics were used. The descriptive statistics include mean, standard deviation, minimum and maximum while the study employed paired sample t-test for inferential analysis. The study reveals that TSA has not enhanced the revenue generation among federal government parastatals in Ekiti state. The research further shows that TSA is counterproductive since average revenue generated after the implementation of TSA is lower than the average revenue that the parastatals generated before the implementation of TSA. The study recommends that the federal government of Nigeria should investigate the reasons why the TSA is counterproductive in Ekiti state and adequately monitor its implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".