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Record W6998582620

From Adjustment to Apportionment in the Goods and Services Tax Act: A Comparative Analysis of the Change-In-Use Rules in Australia, Canada, and New Zealand

2011· article· en· W6998582620 on OpenAlexaboutno aff

Bibliographic record

VenueResearchArchive–Te Puna Rangahau (Victoria University of Wellington) · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsnot available
Fundersnot available
KeywordsApportionmentProduction (economics)Goods and servicesConsumption (sociology)Value (mathematics)Value-added taxCapital (architecture)Quality (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

All countries that have adopted Goods and Services Tax (GST) or Value Added Tax (VAT) employ a ‘change-in-use’ mechanism to distinguish consumption from the stages of production and distribution. New Zealand’s former change-in-use rules were unique. Unlike the ‘use’ based apportionment approaches employed in Australia, Canada and the United Kingdom, New Zealand employed an adjustment approach that utilised a ‘principal purpose’ test and deemed supply mechanism. While Canada has also employed an adjustment approach for capital property, the New Zealand rules have operated differently to those in Canada. In response to criticism for being overly complex and confusing, the New Zealand change-in-use rules will adopt a new ‘use’ based apportionment approach, together with a new mechanism to constrain the number of adjustments, from 1 April 2011 for a number of taxpayers. Applying criteria identified by the Tax Working Group the performance of New Zealand’s change-in-use rules are examined, in comparison to those applied in Australia and Canada. In addition, the comparative readability of the change-in-use provisions in all three jurisdictions is examined. The paper concludes that New Zealand should adopt an apportionment approach and that the Goods and Services Tax Act should be rewritten for improved readability.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.067
GPT teacher head0.266
Teacher spread0.199 · 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 designObservational
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
Published2011
Admission routes1
Has abstractyes

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