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Record W3100503144

The Interpretive Exercise under the General Anti-Avoidance Rule

2020· article· en· W3100503144 on OpenAlexaffabout
David G. Duff

Bibliographic record

VenueeYLS (Yale Law School) · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInterpretation (philosophy)Object (grammar)Supreme courtSubject (documents)LawOrder (exchange)Political scienceDatabase transactionEpistemologyLinguisticsComputer scienceBusinessPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This chapter examines the interpretive exercise under the Canadian GAAR, contrasting this interpretive exercise with ordinary interpretation under the textual, contextual and purposive (TCP) approach, and considering the way in which the object, spirit, and purpose of the relevant provisions is determined in order to decide whether an avoidance transaction is subject to the GAAR. The first part distinguishes the interpretive exercise under the GAAR from the TCP approach, explaining that ordinary interpretation under the TCP approach is rightly constrained by the text of the applicable provisions in a way that the interpretive exercise under the GAAR is not. The second part addresses the way in which the object, spirit, and purpose of the relevant provisions is interpreted, criticizing the “unified textual, contextual and purposive” approach adopted by the Supreme Court of Canada in Canada Trustco Mortgage Co.v. Canada, and arguing that separate inquiries into a misuse of specific provisions and an abuse having regard to provisions read as a whole is not only consistent with the Court’s admonition in Canada Trustco against judicial reliance on overarching or overriding policies that are not anchored in the interpretation of the relevant provisions, but mandated by the text of subsection 245(4).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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 teacher head, not a consensus.

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
Published2020
Admission routes2
Has abstractyes

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