TAX REGIME AND CHALLENGES OF SCALING UP TAX COLLECTION IN NIGERIAN INFORMAL ECONOMY
Bibliographic record
Abstract
The vast and rapid dynamism in economic policies towards improved and speedy policy implementation aided by unrelenting technology capabilities have also transitioned into the tax administration economy.E-taxation is steadily taking the place of manual taxation.Electronic tax system expresses fast, convenient, cost efficient, organized and transparent taxation rather than tasking, time consuming and tax officials-tax payers' corruption ridden operations of manual taxation.This article, engaging exploratory-qualitative research technique, examines multinational peculiarities of automated taxation to draw lessons from issues emanating from implementation, adoption and compliance.It further shed light on challenges of migrating e-tax collection to informal economies in Nigeria.From the extant review of cross-cultural literatures, it was revealed that developed and developing countries are gradually embracing and making constant efforts towards transitioning into a more established e-tax system.Countries like USA, China, Canada, Japan, Russia, Costa Rica, Colombia, and Kenya already joined in adopting etaxation.The paper argued that inadequate database of taxable individuals; expensive internet infrastructure, cyberspace crime and limited awareness among tax payers are drawing back implementation of e-taxation in the Nigerian informal economies, and subsequently suggests practicable policy options.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 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".