Fiscal Devolution and Human Capital Formation in Nigeria: Emphasis on Independent Generated Funds of the Three Tiers of Government
Bibliographic record
Abstract
The benefits of human capital in a nation are enormous and all encompassing. This study investigates the impact of independent generated revenue of the three tiers of government in Nigeria on human capital formation from 2003 to 2017. The purpose is to determine the effect of internally generated revenue of each level of government on human beings in the country. Revenue powers of three tiers of government in Nigeria are the product of fiscal decentralization being practiced in the country. Thus, Ordinary Least Square technique has been employed to perform the multi-regression analysis using Statistical Package for Social Sciences (SPSS) version 20. The findings indicate that federal and local governments’ independent generated funds do not have significant positive impact on human capital development while the state government independent generated revenue exerts significant positive influence on human development index used as proxy for human capital formation in Nigeria. Therefore, the study recommends among others that the three levels of government in the country should strive harder to boost independent revenues for more adequate investment in human capital of the nation.
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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".