Bibliographic record
Abstract
Informed by Aristotle's theory of justice and Reuven Avi-Yonah's views about goals of taxation, this article formulates a reciprocity-based framework for a systematic assessment of the normative merits and effectiveness of taxes, and tests this framework using the diverted profits tax (DPT) and the digital services tax (DST) imposed by the United Kingdom on some multinationals. This assessment is helpful in identifying changes in tax design and other conditions that may be necessary to make a particular tax "just" in terms of absolute or relative substantive justice. Drawing on Aristotle's types of justice, taxes can be classified as universal, distributive, or corrective. From a reciprocity perspective, each tax, depending on its type, contributes to the well-being of a community, but in a different way: universal taxes are contributions to the provision of public goods generally; distributive taxes ensure that members of the community who can pay more contribute more to the provision of public goods; and corrective taxes aim to prevent tax free-riding and seek compensation from free-riders for the harms that they cause by not paying taxes and thus jeopardizing the provision of public goods. This article concludes that the UK DPT is a corrective tax, and it is a just tax. In contrast, the UK DST is likely a distributive tax, and it is just only if its economic burden falls on the firms providing widely used digital platforms. Broadening the tax base would transform the UK DST into a universal tax and change its primary goal from wealth distribution to revenue raising. As a universal tax, the UK DST can be viewed as a just tax because the production and distribution of digital services require extensive use of specific public goods, such as the Internet's infrastructure.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".