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Record W6944074724 · doi:10.17605/osf.io/urj6n

The presumption of resulting trust and beneficiary designations: What’s intention go to do with it?

2017· article· en· W6944074724 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsPresumptionBeneficiaryDoctrineEstateSettlorEmpirical evidence

Abstract

fetched live from OpenAlex

When opening an RRSP or RRIF, investors typically designate a beneficiary. We expect that, when making this choice, most investors intend that their designated beneficiary will indeed benefit from the investment on their death. And further, if there is a dispute between the designated beneficiary and the investor’s estate, we expect investors intend that their choice of beneficiary will prevail. Surprisingly, this is not the case in many provincial appellate courts, which in fact favour the estate in such disputes. More specifically, most Canadian courts apply the presumption of resulting trust to beneficiary designations: they assume, absent other evidence, that the designated beneficiary holds the proceeds of the RRSP or RRIF in trust for the deceased investor’s estate. Only Saskatchewan has taken a contrary position. The Alberta Court of Queen’s Bench in Morrison v Morrison recently weighed both options and endorsed the approach that applies the presumption of resulting trust.In the present article, we analyze the doctrine of resulting trust, its rationale as presented by several leading cases, and empirical evidence evaluating the intentions of Canadian investors. We conclude that applying the presumption of resulting trust to beneficiary designations betrays both the theory and purpose of the presumption. It also runs counter to the intentions of most Canadians and creates uncertainties in millions of beneficiary designations. Finally, we present several solutions for bringing the law in line with the intentions of investors, and indeed common sense.

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)0.015
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.594

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0110.038
Scholarly communication0.0140.012
Open science0.0040.004
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0040.001

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 head0.069
GPT teacher head0.414
Teacher spread0.345 · 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 designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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