Assessment of Risk in the Internally Generated Revenue (IGR) Structure of Abia State, Nigeria
Bibliographic record
Abstract
The study assessed risk in the Internally Generated Revenue structure of Abia State, Nigeria. The specific objectives included (I) estimation of factors and significant input variables influencing risk in IGR structure of the State and (II) examination of risk reducing practices. Data which were collected from 50 management staffs of revenue yielding Ministries, departments and agencies in Abia State were analysed with factor analysis, Tobit regression analysis and descriptive statistics. The results of the factor analysis grouped the significant input variables which scored 0.33 and above into institutional and non-institutional sources of risk while that of Tobit regression analysis revealed that significant variables which cause variation between expected and realised IGR of the State are lack of database; mismanagement of fund by government; delay in payment of revenue by tax payers; difficulty in tracking tax evaders; and weak internal control mechanism. The results point to the fact that policy on tax identification number (TIN) should be strengthened and linked to the bank account of the tax payer so that relevant revenues are deducted at source.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".