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Record W2964194326

GIFTING CULTURAL PROPERTY IN CANADA: TESTING A TAX EXPENDITURE

2007· article· en· W2964194326 on OpenAlexaboutno aff
Steven L Nemetz

Bibliographic record

VenueThe Canadian Bar Review · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLegal and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPublic economicsCultural propertyProperty taxBusinessIncentiveStatutory lawValuation (finance)Income taxInternational taxationLaw and economicsDouble taxationTax reformIndirect taxAccountingEconomicsLawCultural heritageMarket economyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The Canadian Income Tax Act provides a unique system of tax incentives to encourage the disposition of cultural property to public institutions by way of donation or sale. These tax incentives comprise a tax expenditure designed to indirectly support government policy objectives with respect to art and cultural property. This cultural property program is limited to gifts of particular property — “certified cultural property” — to “designated institutions.” The income tax incentives associated with disposition of cultural property are part of a larger statutory scheme under the Cultural Property Export and Import Act which controls the export out of and import into Canada of cultural property. At the centre of this statutory scheme is an administrative body, the Canadian Cultural Property Export Review Board, which plays a key role in fulfilling the objectives of the Cultural Property Export and Import Act and has a unique role in the administration of the income tax incentives pertaining to the disposition of cultural property; in particular in its responsibility for determining value for the purpose of issuing cultural property certificates under the Income Tax Act. Disputes over valuation of cultural property have been the focus of the debate amongst the stakeholders and in the Courts this is where this tax expenditure has been tested and recent decisions have determined its limits.

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 score0.975
Threshold uncertainty score0.337

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 head0.077
GPT teacher head0.221
Teacher spread0.144 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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