RE-DEFINING NIGERIAS TAX SYSTEM AMIDST DIGITALISATION OF THEBUSINESS ENVIRONMENT
Bibliographic record
Abstract
Technology, internet and e-commerce have redefined business models and practices such that values are created in environments different from where profits are earned and taxes are subsequently paid. Suchpractices, which exacerbatebase erosion and profit shifting, negatively affect the collectible tax revenues by governments in several jurisdictions including Nigeria,making it difficult for them to meet their social contract obligations to their citizens.Using an expost facto research design and qualitative research methodology, this exploratory study assessed the capacity of the Nigerian government to address the challenges imposed on its tax system by the emerging digitalized economy. The study observed that the issue of taxation in digitalized economy has not received the desiredlegislative and governancepriority attention largely because of the dearth of knowledge about its complexities as well as the undue dependence on revenue from crude oil.The study therefore recommends that the revenue authorities should set up a think tank comprising chartered accountants, tax practitioners, information technology experts, academics and regulators to develop a comprehensive framework to address the issue from a national perspective while the ECOWAS Commission should be prodded and supported by its member-states to take on the issue at the sub-regional level as OECD is currently doing for its member-states in Europe, USA, Japan and Canada.
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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 | 0.003 |
| 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 | 0.004 | 0.003 |
| 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".