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Record W3028919985 · doi:10.5040/9781509923106

Beneficial Ownership in Tax Law and Tax Treaties

2020· book· en· W3028919985 on OpenAlexaboutno aff
Pablo Andrés Hernández González-Barreda

Bibliographic record

VenueHart Publishing eBooks · 2020
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsnot available
Fundersnot available
KeywordsTax lawEquity (law)Tax reformLaw and economicsBeneficiaryCommon lawIncome taxPolitical scienceTax avoidanceLawPublic economicsBusinessEconomics

Abstract

fetched live from OpenAlex

This book explores the concept of beneficial ownership in equity law, the domestic tax laws of the United Kingdom, Canada and the United States, as well as its varied and increasing uses in international tax law. By analysing the evolution of beneficiary rights in equity and the use of beneficial ownership wording in tax law, the book draws a roadmap for dealing with beneficial ownership in both national and international tax law. This approach highlights those common misconceptions that can be avoided by understanding the origins of the concept and its engagement with equity, as well as the differences with tax law. However, the book does not limit itself to dealing with theoretical discussion, but also offers an instructive and detailed practical case study. Offering both academic commentary and a practitioner focus, the book will be of the utmost interest to scholars and practitioners from common and civil law countries

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.007
Scholarly communication0.0060.005
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.002

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.

Opus teacher head0.030
GPT teacher head0.214
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations6
Published2020
Admission routes1
Has abstractyes

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