Qualification of Taxable Entities and Treaty Protection
Bibliographic record
Abstract
This report was prepared for the 2014 International Congress of the International Fiscal Association. The general reporters for the Congress asked IFA branches around the world to prepare a report designed to provide information on how countries address (1) the question of when domestic and foreign entities are treated as transparent or taxable and (2) conflicts between different countries’ treatment of entities as transparent or taxable for treaty purposes. This report constitutes the IFA U.S.A. Branch’s submission to the general reporters.\nThe report is divided into two sections. The first section of the report provides a general description of how both domestic and foreign entities are classified under U.S. federal tax law. The second section of the report focuses on how the United States deals with conflicts in entity classification when applying tax treaties. This section of the report consists of an analysis of a series of different scenarios posed by the general reporters to all IFA branches where different countries classify the same entity differently (i.e., one or more countries treat the entity as taxable while one or more other countries treat the entity as transparent).
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 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".