Bibliographic record
Abstract
At a superficial glance, Internal Revenue Code Section 892 appears to favor sovereign wealth funds (SWFs) over foreign private investors by exempting the former from tax on a significant range of US investments. This has recently led to calls for its abolition. Several authors, however, have challenged this view by pointing out that the impact of US tax on the relative competitiveness of SWFs and private investors should be analyzed in terms of the investors’ comparative, not absolute, advantage. And such analysis hinges on whether foreign private investors are taxed by their home countries on a worldwide basis, as well as on how SWFs are taxed in other countries where they invest. In support of these challenges, I discuss two hitherto under-noticed facts: the prevalence of the practice of worldwide taxation among countries generating the most investments into the US, and the fact that SWFs themselves may be taxed at home. Both buttress the conclusion that current US tax law is unlikely to have disadvantaged private investors. Moreover, the institutional characteristics SWFs imply that they lie in between foreign government pension funds and commercial state-owned enterprises. Changing current US tax law would unjustifiably hurt the former group of foreign investors, while having no policy effect on the latter, more controversial group of investors.
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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.022 | 0.036 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".