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Investment Arbitration and State-Driven Reform

2022· book· en· W4283312261 on OpenAlex
Wolfgang Alschner

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOxford University Press eBooks · 2022
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsArbitrationState (computer science)Investment (military)Investment arbitrationBusinessLaw and economicsPolitical scienceEconomicsComputer scienceLawForeign direct investmentInternational investmentAlgorithm

Abstract

fetched live from OpenAlex

Abstract This book reviews the first set of investment arbitration awards rendered under a new generation of investment treaties that actively balances investment protection and host state flexibility and finds that state-driven reform is being rolled back through an arbitral backlash as new investment agreements reproduce old interpretive outcomes. Combining robust empirical and computational analysis, new comprehensive datasets on investment treaties and awards, and a range of theories from law and economics to complexity science, this book proceeds in three steps. First, it traces state-driven reforms of investment treaty design over seven decades. Second, it demonstrates that these reforms are undermined in practice as tribunals rely on most-favored-nation treatment clauses, customary international law, and precedent to interpret new treaties like old ones. Third, the book suggests how states can preserve and amplify the impact of state-driven reforms by leveraging forward-looking interpretation, data-driven renegotiation, and tax-style multilateralization to modernize old treaties in light of new ones.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.018
GPT teacher head0.190
Teacher spread0.172 · 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