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Record W4319048415 · doi:10.1093/tandt/ttad002

Managing and preventing cross-border disputes arising with the increasingly popular use of succession substitutes

2023· article· en· W4319048415 on OpenAlexaff
Jeffrey Talpis

Bibliographic record

VenueTrusts & Trustees · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicConflict of Laws and Jurisdiction
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsProbateChoice of lawPopularityBusinessCommon lawEcological successionCivil law (Civil law)LawLaw and economicsLeasehold estateInterimProperty (philosophy)Conflict of lawsCommercial lawEconomicsPolitical science

Abstract

fetched live from OpenAlex

Abstract As the world becomes increasingly interconnected, many people are faced with increasing challenges in the planning and execution of wealth-transfer strategies upon death when they have property in different countries. At the same time, the rising popularity of succession substitutes or, as they are called in the common law world, will substitutes or non-probate devices that transfer property at death otherwise than by succession, is increasingly posing challenges for cross-border legal disputes, particularly in the absence of uniform choice of law rules relating to succession substitutes. In this article, Professor Jeffrey Talpis reviews the existing approaches for managing and preventing cross-border disputes involving, for example, the use by parties in civil law jurisdictions of common law non-probate devices, such as acquisitions in joint tenancy, pay-on-death or transfer-on-death accounts, and revocable inter vivos trusts. The author first defines and classifies succession substitutes used in domestic laws, then examines different approaches for managing cross-border disputes involving these devices in civil law and common law jurisdictions. While the author continues to promote the adoption of an international instrument to provide uniform solutions to resolve cross-border disputes arising from the use of succession substitutes, he proposes certain suggestions that, in the interim, could assist prevention of such conflicts of law.

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)0.017
metaresearch head score (Gemma)0.033
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.007
Scholarly communication0.0070.007
Open science0.0020.008
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.363
Teacher spread0.337 · 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
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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