Cláusula de la nación más favorecida en los CDI: herramienta para la potencial disminución de las retenciones del impuesto a la renta aplicables a Chile y Canadá
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".