IMPROVING TAX ADMINISTRATION: A CASE STUDY OF THE UGANDA REVENUE AUTHORITY
Bibliographic record
Abstract
KANGAVE, JALIA, Improving tax administration: a case study of the Uganda Revenue authority, Journal of African Law, 49, 2 (2005): 145–176 The prevalence of poverty in developing countries demands that these countries should improvise internal revenue generating projects to supplement, or better still, ultimately significantly reduce dependence on foreign funding. This way self-sustaining economies will be built. One such internal revenue-generating mechanism, and perhaps the most commonly used, is taxation. This paper makes a case for tax administration as a tool of increasing the contribution of tax revenue to Gross Domestic Product, and consequently, a means of reducing the gap between the rich and the poor. The goal of this paper is to propose ways in which the Uganda Revenue Authority (the URA) can improve its tax administration. To achieve this objective, the paper begins with a detailed discussion of the URA's structure and the procedures it follows in collecting taxes. It then highlights the problems that may arise from such structure and procedures, before making proposals on how the URA can reform its organizational structure and processes to maximize its potential in revenue collection capabilities.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".