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Record W4389283327

The most-favoured-nation clause in tax treaties: tool for potential reduction of withholding income tax applicable to Chile and Canada

2013· article· en· W4389283327 on OpenAlexaboutno aff
Renée Antonieta Villagra Cayamana, Jorge Antonio Villagra Cayamana

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsnot available
Fundersnot available
KeywordsWithholding taxIncome taxEconomicsReduction (mathematics)Tax treatyDirect taxPublic economicsDouble taxationInternational economicsInternational tradeLaw and economicsBusinessPolitical scienceMathematics
DOInot available

Abstract

fetched live from OpenAlex

Tax treaties to avoid the double taxation signed by a country have consequences for the future, but they can also modify the terms of treaties that are already in force, in case these contain most-favoured-nation clauses. In this line, taxpayers and companies, particularly, as well as the Tax Administration must be alert, regarding topotential modifications of the terms of the Peruvian tax treaties already in force; mainly about the withholding tax rate applied to royalties in the Convention subscribed with Chile and the withholding tax rates applied to dividends, interests and royalties in the Convention subscribed with Canada, taking into account that both of the mentioned tax treaties contain most-favoured-nation clauses for those kind of income. The Ministry of Economy, as the entity in charge of negotiations of the bilateral conventions, according to Law Decree 25883, has the responsibility of negotiating future treaties with full knowledge that the terms to be included could also cause the effect to decrease the withholding tax rates of the income tax in respect to conventions already in effect, as a consequence of the most-favoured-nation clause they contain.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.412
Teacher spread0.338 · 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 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
Published2013
Admission routes1
Has abstractyes

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