A Principles – Based Assessment of The Quality of Zimbabwe’s Direct Tax Policy for The Digital Economy
Bibliographic record
Abstract
Background: In 1998, under the Ottawa framework, the OECD and non-OECD countries agreed that any new taxation rules should adhere to the guiding principles, namely Neutrality, efficiency, certainty and simplicity, effectiveness, fairness flexibility (Cockfield, 2006). In the absence of an international consensus-based – taxation framework for the digital economy, a question arises on whether unilateral measures adopted by countries such as Zimbabwe comply with the principles of a good tax policy. Objective: The study aimed to examine the quality of Zimbabwe's direct tax policy for the economy based on the principles of a good tax policy prescribed by the Organisation for Economic Cooperations and Development (OECD). Method: The study was carried out under a pragmatic philosophical view and adopted a quantitative cross-sectional survey as the research design. Data collection was done using closed-ended questionnaires. The study population comprised 250 tax experts drawn from the Zimbabwe Revenue Authority (ZIMRA) representing tax administrators and private sector tax practitioners representing the taxpayers. Quantitative data was collected from a sample of 146 respondents. Systematic random sampling was used to select the respondents. Chi-squared test was used to analyze the data in SPSS. Results: The study revealed that among the overarching principles of a good tax policy, namely (1) Fairness; (2) Certainty and Simplicity; (3) Neutrality; (4) Efficiency, and (5) Effectiveness, Zimbabwe's tax policy for the digital economy only complies with the principles of Fairness, Certainty and Simplicity. Conclusion: The study established that, to a greater extent, Zimbabwe's tax policy for the digital economy needs to comply with the principles of a good tax policy. Keywords: digital economy; OECD, principles; taxation; Zimbabwe
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".