Sovereign Immunity and Source State Taxation of Sovereign Wealth Funds: Is It Time to Re-Evaluate?
Bibliographic record
Abstract
Cross-border investments of states have rapidly increased over the last few years and are, more often than not, structured through special purpose investment funds or arrangements, known as sovereign wealth funds (SWFs). The total value of assets under the management of SWFs is currently estimated at USD 7.1 trillion (as at March 2016). In relation to states, their subdivisions and their wholly owned entities, the OECD Commentary mentions the customary international law principle of sovereign immunity. According to this principle, a foreign sovereign state can be held immune from the jurisdiction of the courts of another sovereign state in civil proceedings (jurisdictional immunity), and this principle may also apply to state-owned entities. A number of states, including Australia, Canada, the United Kingdom and the United States, apply the sovereign immunity principle to taxation as well. SWFs might also benefit from these tax immunities. The preferential tax treatment over other (private) investors to which a tax immunity regime potentially gives rise has historically been explained (or justified) by reference to the sovereign immunity principle as a principle of customary international law. However, an examination of the tax immunity regimes and the rules on jurisdictional immunity in all four states strongly suggests that the tax exemptions accorded to foreign sovereigns and SWFs are not (or, at least, are no longer) truly motivated by sovereign immunity. As a result, these states, and other states in which a comparable situation exists, would need to re-evaluate their existing tax immunity framework.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.014 |
| 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".