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Record W2948860978

Indigenous Settlement Trusts: Recharacterizing the Nature of Taxation

2019· article· en· W2948860978 on OpenAlexaboutno aff
Frankie Young

Bibliographic record

VenueAppeal: Review of Current Law and Law Reform · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLegal principles and applications
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)RevenueBusinessGovernment (linguistics)Tax avoidanceTax reformAd valorem taxPublic economicsLaw and economicsFinanceEconomicsDouble taxationPayment
DOInot available

Abstract

fetched live from OpenAlex

This article examines how Indigenous Settlement Trusts (“Settlement Trusts”), established for the benefit of First Nations, are affected by the Income Tax Act (“Tax Act”). I argue that Settlement Trusts should be taxed differently than other personal trusts under the Tax Act. Currently, a Settlement Trust is taxed just as any other trust—as an individual pursuant to section 104(2) of the Tax Act—regardless of whether distinguished circumstances exist, as in the case of Indian Act-recognized Bands. This means that revenues are taxed at the top personal marginal tax rate. As such, the Settlement Trust relies upon the application of section 75(2) of the Tax Act to attribute revenues that remain in the Trust to the Band. The revenues are then tax exempt, pursuant to section 149(1)(c) of the Tax Act, because the Canada Revenue Agency’s administrative position is that Bands are public bodies performing a function of government in Canada. Ultimately, Bands face unnecessary administrative processes and costs from having Settlement Trusts taxed in this manner. I conclude that the Federal government should amend the Tax Act to exempt Settlement Trust revenues from being taxed under the general trust tax provisions so that administrative costs and processes for Bands will be eliminated or at least minimized. Bands could then claim Settlement Trust revenues directly in the same manner that they claim all other Band revenues.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.996
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.339
Teacher spread0.318 · 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.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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