Ventajas de los convenios de doble imposición tributaria en la contribución del desarrollo del país.
Bibliographic record
Abstract
The research developed aims to analyze the advantages it provides to the economy the signing of agreements to avoid double taxation, for which is to review the double taxation agreements that Peru has signed with countries such as Chile, Canada and Brazil, which consist in an article for the elimination of double taxation, in the case of double taxation agreements with Chile and Canada can be found in Article 23 and Article 22 with Brazil, which is a tool to address international tax evasion. As result has found that foreign investment in Peru in the year 2010 was 20 781 million dollars has like the largest investors Spain, United Kingdom and United States with 21.2%, 21.04% and 15.24% respectively, grow the way 7285 million to 20781 million dollars 1997 to 2010, and according to the table of macroeconomic indicators the gross investment so far of 2010 obtained 25.1% of PIB, being the private investment most important with a share 19.2%, was revised agreements with Canada, Brazil and Chile, doing know how to avoid double taxation. We used the method of documentary analysis, analysis of investment behavior at the level of Latin America and the country that matches the increase in global agreements. Finally, this research to give known the advantages over double taxation agreements for the contribution of economic development of country.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".