Revenue Generation in Lagelu Local Government Area of Oyo State: A Correlate of Tax Mobilization and Utilization
Bibliographic record
Abstract
This paper examined sources of tax revenue and its utilisation in Lagelu Local Government Area of Oyo State. It further identified various bottlenecks associated with mobilising local taxes with a view to evaluating tax mobilization and utilisation in Lagelu Local Government Area of Oyo State. The study employed primary and secondary sources of data, and a sample size of 170 representing 79% of the target population. The study revealed that tax revenues of Lagelu Local Government Area are often sourced from fees and charges like fines (58.2%); right of occupancy (46.5%); motor park/market/transport (45.3%). Further, the results revealed that tax revenues are mainly utilised on personnel cost (64.7%); education (57.1%); health and medic (45.9%). Various bottlenecks associated with tax mobilisation include absence of basis utilities (88.9%); misappropriation of public fund (82.3%); high rate tax evasion (82.3%). The study concluded that tax mobilisation of the council was not optimally explored as perceived money-spinning sources of tax revenue to the local council such as tenement rate, and shops and kiosks rate were not often mobilised. Thus, it was suggested that to reduce bottlenecks in tax mobilisation, local government officers should be more transparent while adequate training should be provided for the tax collectors in order to raise tax mobilisation and utilisation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".