Bibliographic record
Abstract
Tax income is one of the major sources of revenue for all the three tiers of government in Nigeria.Ever since global fall in oil price, governments at all levels have been striving to increase their internally generated revenue.Tax administration has been characterized with a lot of malaises which encourage gross tax evasion and low compliance.Federal and state governments adopted the option of engaging tax agents or tax consultants in collection of taxes in their own jurisdiction.The involvement of tax consultants has yielded positively in increasing the level of compliance and reduces the rate of tax evasion and tax avoidance.With the low tax base of the last tier can tax consultants be involved in collection?In lieu of this, this paper discusses prospect, problem and settlement of tax consultancy service in revenue generation in grassroots in Nigeria.The study hinged on optimal tax theory and theory of outsourcing.Descriptive research is used.Hypotheses were formulated and tested using chi square of Statistical Packages for Social Science.It was concluded that tax consultancy has great impact on revenue generation in the grassroots.The paper recommended the use tax consultants in the grassroots to foster revenue generation at the grassroots and tax consultancy service should be given legal backing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.966 | 0.962 |
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".