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Policy Forum: The Australian Experience with Preferential Capital Gains Tax Treatment—Possible Lessons for Canada

2021· article· en· W4210270753 on OpenAlexvenueaboutno aff
John Minas, Youngdeok Lim, Chris Evans, François Vaillancourt

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsInclusion (mineral)Capital gains taxCapital (architecture)Perspective (graphical)Public economicsEconomicsRelevance (law)Capital incomeTax policyLabour economicsMonetary economicsPolitical scienceDouble taxationTax reformInternational taxationAd valorem taxSociologyLawGeography

Abstract

fetched live from OpenAlex

This article compares the preferential tax treatment of capital gains in Australia and in Canada, with a view to determining whether there are any lessons from the Australian experience that may be of relevance to Canada. The tax treatment of capital gains is similar in the two jurisdictions in that both apply a 50 percent inclusion rate or the equivalent. Several aspects of the taxation of capital gains in Australia might be considered cautionary from the Canadian perspective. The Australian experience indicates that winning support for an increase in the capital gains inclusion rate can prove difficult, as demonstrated by the unsuccessful proposal by the Australian Labor Party, during the 2019 federal election campaign, to effectively raise the inclusion rate to 75 percent.

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)0.010
metaresearch head score (Gemma)0.018
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: none
Teacher disagreement score0.122
Threshold uncertainty score0.887

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0220.005
Scholarly communication0.0100.004
Open science0.0030.004
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0090.000

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 head0.035
GPT teacher head0.234
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 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

Citations1
Published2021
Admission routes2
Has abstractyes

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