Bibliographic record
Abstract
This article examines digital services taxes (DSTs) from an international trade law and policy perspective, challenging prevailing narratives about their protectionist nature and offering alternative frameworks for their evaluation. The analysis begins by establishing that tariffs and discriminatory taxes on cross-border trade in goods and services remain permissible absent specific trade commitments, with many governments actively using these tools to advance a variety of policies. The article demonstrates that DSTs can serve legitimate policy objectives, and contrary to critics’ assertions, neither platform market characteristics nor implementation contexts support claims of protectionist intent. While acknowledging that digital platform markets often exhibit monopolistic tendencies, the article argues that DSTs’ structural features and inherent information asymmetries typically preclude their effective use as rent-snatching or profit-shifting instruments. Drawing on terms-of-trade theory, the analysis reveals why most nations would likely not benefit from a multilateral prohibition of DSTs, and how DSTs applied to digital platforms may enhance national welfare. The article proposes that, similar to the development of international rules relating to tariffs, rules will naturally evolve to coordinate DSTs, which may also reduce the risk of excessive taxation. Finally, the analysis introduces a novel perspective by examining DSTs through the lens of subsidy regulation, offering an alternative theoretical foundation for their implementation. This comprehensive analysis contributes to the scholarly discourse by providing a nuanced understanding of DSTs within the broader context of international trade and international tax regimes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".