Challenges of Expanding Internally Generated Revenue in Local Government Council Areas in Nigeria
Bibliographic record
Abstract
Availability of adequate financial resources are desirous for any organization to achieve the purposes for which it is established. Local government councils in Nigeria are created statutorily to perform clearly assigned functions. Experience has however demonstrated that these councils have fallen short of achieving the objectives for which they were indorsed. Some reasons have been espoused by scholars for the failing performances of most local government councils in Nigeria. Against this backdrop, this study seeks to posit that local government councils are likely to achieve their set objectives to a large extent if their internally generated revenue (IGR) are expanded. Also, the study seeks to postulate the capacity of local government councils in Nigeria to sustainably expand their internally generated revenue (IGR) is inhibited by the kind of strategies adopted and by some critical challenges facing them. To enable the explication of the assumption, the study adopts a conscious survey of relevant literature on our subject matter. The data generated are systematically analyzed to verify the validity of the above assumptions. The study maintains that apart from the fact that the fiscal federalism apparently seem unfavourable to the local government functional responsivities, it has nevertheless the provided for adequate source for their internally generated revenue to augment the federally allocated funds. Again, sundry factors hinders the expansion internally generate revenue have been identified and recommendations for boosting IGR in local government councils in Nigeria have also been articulated.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".