Challenges that the commissioner for the South African Revenue Service’s information gathering powers pose to taxpayers’ rights
Bibliographic record
Abstract
This study is about the importance of the duty of the South African Revenue Service (“SARS”) to recognise and protect taxpayers’ rights. Particular focus is placed on the challenges posed with respect to taxpayers’ rights when the Commissioner for SARS (“the Commissioner”) exercises his information gathering powers. This study covers the manner in which the gathering of information by SARS is conducted domestically and internationally and the purposes for which SARS uses that gathered information. \nThe term “information gathering” is not defined in the Income Tax Act 58 of 1962 (“ITA”) or the Tax Administration Act 28 of 2011 (“TAA”). It may, however, be understood to mean the way in which the Commissioner gathers or collects information from taxpayers. The purpose of this information gathering may be for the Commissioner to measure taxpayers’ compliance with their obligation to pay tax. \nThe Commissioner may use the following ways or methods to gather information from taxpayers: records and books, tax returns, request for information from taxpayers or third parties, inspection, verification or audit, search and seizure, exchange of information with other countries, Country-by-Country Reporting, the Voluntary Disclosure Programme and the Reportable Arrangements provisions. \nThe study discusses how taxpayer’s constitutional rights may be infringed when the Commissioner uses these various methods to gather information from taxpayers. The study also discusses the effectiveness of the remedies or avenues available to the taxpayers whose rights have been infringed. \nThe study entails a comparative study of the United Kingdom (“UK”) and Canada, and compares the relevant positions in these two countries on the above matters with the position in South Africa. The aim of this comparison is to develop recommendations for solving the challenges identified in South Africa.
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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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".