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Record W2810110334 · doi:10.1007/978-94-6265-243-9_9

The Growing Tendency of Including Investment Chapters in PTAs

2018· book-chapter· en· W2810110334 on OpenAlexaboutno aff
Maxim Usynin, Szilárd Gáspár-Szilágyi

Bibliographic record

VenueNetherlands yearbook of international law · 2018
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersNatural Environment Research CouncilNorges ForskningsrådDanmarks Frie Forskningsfond
KeywordsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

In the context of a rising number of preferential trade agreements (PTAs) that include investment protection provisions traditionally found in bilateral investment treaties (BITs), this chapter has a double purpose. First, based on an empirical analysis of 158 post-North American Free Trade Agreement (NAFTA) PTAs, we conclude that three categories of countries/regional economic integration organisations (REIOs) exist: those that regularly include investment chapters into their PTAs (Japan, the United States, Canada, the Association of Southeast Asian Nations (ASEAN), Australia and the Caribbean Community (CARICOM)), those that are finding their voice in international investment law and increasingly include such chapters (India, China, the European Union and Chile) and those that have an adverse position towards it (Brazil and the Southern Common Market (MERCOSUR)) or defer the inclusion of such provisions to further negotiations (African Plurilaterals, Morocco and South Africa). Second, we look at the drivers behind including/excluding investment protection provisions in/from PTAs. Some drivers will be readily apparent from the data collected for the purpose of answering the first question, while other drivers will need a more detailed discussion. These drivers are: (a) the weaker party accepts/uses templates of more powerful states; (b) states/REIOs wish to pursue more comprehensive and resource-friendly negotiations; (c) states/REIOs want to achieve a more coherent application of international economic law.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.230
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2018
Admission routes1
Has abstractyes

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