Legal Framework for Regulation of Income Tax on Cryptocurrency Transactions Based on the Principle of Justice: Comparative Legal Study with Canada
Bibliographic record
Abstract
The Regulation of the Minister of Finance of the Republic of Indonesia Number 68/PMK.03/2022 as the legal basis for cryptocurrency income tax does not reflect the principle of fairness because the consideration is based on the principle of ease of administration. This paper aims to provide an alternative income tax legal framework on cryptocurrency based on the principle of justice. It is expected to be a step to increase state revenue through the sector of cryptocurrency tax. This paper employs a conceptual and comparative approach to normative research. Furthermore, the researcher compares income tax regulations and policies on cryptocurrency in Indonesia and Canada with the theory of justice to obtain answers to legal problems. The Regulation of the Minister of Finance of the Republic of Indonesia Number 68/PMK.03/2022 does not reflect the principle of justice because the final tax rate does not reflect the tax burden. In addition, there are limitations on the tax collector's authority, so tax collection is not comprehensive. Therefore, this paper compares and analyses income tax regulations and policies in Indonesia and Canada to obtain several alternative forms of fair tax legal framework on cryptocurrency. Alternative cryptocurrency income tax regulation that can be accommodated by the government is to change to a progressive rate to fulfill tax fairness, change the collection system to a self-assessment and do not differentiate the source of income and also cooperate with various exchanges to exchange transaction data to prevent criminal acts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".