Bibliographic record
Abstract
The Global Guide to Trusts represents the work of a team of leading outstanding tax practitioners active in 28 jurisdictions under the editorship of an internationally recognized trust expert. The Global Guide to Trusts facilitates the understanding of international tax issues and provides specific guidance in such facets of international taxation of trusts as the following: Tax treatment of the creation of a trust Tax treatment of income and capital gains Tax treatment of distribution from a trust to its beneficiaries Tax implications of settlor’s death Tax implications of the termination of a trust Reporting obligations The second edition 2020 covers 7 new jurisdictions (Austria, China, Cyprus, Liechtenstein, Luxembourg, Malta, South Africa) and expands on new developments: Sham trusts Applicability of tax treaties to trusts Applicability to trusts of anti-avoidance tax rules Trustees regulation Registers of trusts 28 Jurisdictions includes: Argentina, Austria, Australia, Belgium, Brazil, Canada, China, Cyprus, France, Germany, Hong Kong, India, Indonesia, Israel, Italy, Liechtenstein, Luxembourg, Malta, Mexico, Netherlands, New Zealand, Portugal, Russia, Spain, South Africa, Switzerland, U.K., U.S.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.422 | 0.347 |
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".