Bibliographic record
Abstract
The increasing cost of running government coupled with dwindling oil-revenue has left various tiers governments in Nigeria -Federal, State and Local Governments with the need to evolve strategies to improve their revenue base.The bulk of most state's revenue today comes from allocations from the federation account and value added tax and just minimally augmented with internally generated revenue (IGR) from taxes.There is therefore urgent need for the state government to consider more alternatives for revenue generation through which they can enhance their internally generated revenue.This paper is set out to examine ways of enhancing internally generated revenue (IGR) in states.The sub-objectives are to: Examine the current level of revenue generation in the states; Identify challenges that have impeded sufficient internal revenue generation in the states, and; to advance strategies that will enhance internal revenue generation in the states.The paper is descriptive and used only secondary date.It adopted the fiscal federation theory as the theoretical framework.The findings are that: internally generated revenue constitutes just a small proportion of the state finance; the current system of revenue generation is fraught with problems; the revenue base of the states is uneven, so narrow and need to be diversified.To enhance internal revenue generation, strategies such as establishment of a dependable data base which is accessible is required, eliminating all sources of revenue leakages through the automation of revenue collection system, tracking the underground economy for more revenue generation, diversification of the revenue base through wealth creation among others are necessary panacea.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.954 | 0.948 |
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".