A regulação do investimento estrangeiro direto e suas implicações para o caso brasileiro
Bibliographic record
Abstract
In the last decades Brazil adopted a successful policy of attracting foreign investment which can be attested by the fact that in 2017 we become the 4th largest recipient of foreign direct investment in the world. Our need to increase investments rate as well as to finance our current account deficits are the main reasons that made this policy so active. Nevertheless, the absence of control over the inflow of foreign investments does not allow the country to assess the impacts they have on its development and adherence to national interests such as autonomy, national defense, technology or account balance sustainability. Thus Brazil is going against the world trend that has been the creation or strengthening of instruments that regulate the entry of foreign investment precisely in order to ensure that they comply with these objectives. The great motivator for the strengthening of these controls has been the participation of China in the international scene with the implementation of the "China 2025" program, which seeks to make it a relevant player in sectors such as aerospace, robotics, integrated circuits or energies including by the acquisition of international companies holding such technologies. The present work therefore seeks to discuss and analyze whether Brazil has a regulatory framework for the entry of foreign investment adequate for the development of the country which also takes into account its national interests. In order to do so, we present how foreign direct investment can impact our economy, how China has appeared on the international scene and how this has been reflected in the regulation of foreign investment, especially in countries such as USA, European Union, China, Australia and Canada, and finally, how Brazil has prepared itself and what mechanisms it might use to improve the use of these resources for its development.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".