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Record W3174519251 · doi:10.31857/s086904990015419-2

Investment Regulations in the United States – Mexico – Canada Agreement

2020· article· en· W3174519251 on OpenAlexaboutno aff
Margarita Perova

Bibliographic record

VenueObshchestvennye nauki i sovremennost · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsInvestment (military)Free trade agreementBusinessConfidentialityAdministration (probate law)Investor-state dispute settlementInternational tradeState (computer science)Financial servicesService (business)Foreign direct investmentInternational economicsFinanceEconomicsInternational investmentFree tradeLawMacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

This article focuses on the special feature of investment regulations contained in the United States–Mexico–Canada Agreement (USMCA). This agreement aims to replace and modernize the North American Free Trade Agreement (NAFTA). The author analyzes U.S. administration’s approach to the development of this agreement, as well as the new elements in USMCAs’ investment regulations. US administration’s approach is based on the US trade deficit indicator, which is used to measure the agreements’ success or failure. This approach might be counterproductive. Trade imbalances are determined by underlying macroeconomic fundamentals. Mostly, the relationship between saving and investment within each country plays a big part. The biggest change from NAFTA is the curtailment of investor-state dispute settlement (ISDS). The US administration argued that without the recourse to ISDS provided to companies overseas, companies would be more inclined to invest in the United States. Thus US investors loose robust international law protection. This agreement introduces new elements in the regulation of investments in the financial services sector, while focusing on expanding customer service capabilities and efficiency gains, taking into account the impact of technological innovations. The most important provisions are the prohibition of local data storage requirements, data protection and confidentiality of personal information exchange, the use and location of computing facilities by financial institutions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.032
GPT teacher head0.217
Teacher spread0.185 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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