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Record W6995469394

O modelo ACFI revisado : aprimoramentos ao regime internacional de investimentos do Brasil,

2021· dissertation· en· W6995469394 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Library of Theses and Dissertations (Universidade de São Paulo) · 2021
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTreatyInvestment (military)European unionForeign direct investmentInvestment protectionInternational investment
DOInot available

Abstract

fetched live from OpenAlex

In 2013, the Brazilian government approved its Cooperation and Facilitation Investment Agreement (CFIA) model. It has declared that the CFIA model integrates the most recent trends in investment policies, materializing the governments intention to build a new approach in the promotion and protection of investments. However, the CFIA model is only partially supported by academia and largely criticized by domestic and foreign investors and capital-exporting countries. The purpose of this study is to scrutinize the CFIA model to identify how it the balances States need for policy space and investors right for protection to propose the way forward for the Brazilian international investment regime. The CFIA model is evaluated holistically through its institutional design and main substantive and procedural provisions to give robustness to the investigation. Analyzing these intertwined elements combined determines the value of an investment framework, not the evaluation of its components in isolation. To successfully deal with the main apprehensions about the CFIA model, the stateof-art literature on ways to overcome the challenges faced by the international investment regime has served as a basis of assessment for elaborating a revised Brazilian regime. The study has also considered the criticism and lessons learned from the international and the Brazilian experiences with the traditional bilateral investment treaty (BIT) model, and the institutional choices and procedure and substantive disciplines found in the European Union (EU) model of investment treaty and the United States-Mexico-Canada Free Trade Agreement, attempting to understand the reasons for their institutional design, and drawing analogies and distinctions between the models and other international regulations. The EU and the United States are among the leading players searching for ways to improve the international investment regimes structure, disciplines, and rules. This study concludes that the Brazilian treaty-language fails to provide legal certainty and the model has overshot in its effort towards correcting the unbalance between States need for policy space and investors right for protection in the traditional BIT model. The study also presents several intertwined proposals for systemic reform of the Brazilian international investment regime. The proposals can contribute to constructing a renewed Brazilian regime that fosters the political and legal stability and predictability necessary for domestic and foreign investors to engage in new or additional investments while safeguarding the pursuit of competing public policy concerns. They enhance democratic legitimacy, fairness, and the rule of law, improve resource allocation efficiency, reduce diplomatic confrontation and protect fundamental and human rights, recognizing investment treaty as a tool rather than an obstacle to sustainable development. Several proposals can be easily adopted by the Executive and lately approved by Congress. However, to strengthen protection for investors while safeguarding public policy space, the Brazilian government must design its reformed regime transparently and inclusively to ensure the necessary support for its adoption and later entry into force.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.004
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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