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Record W4404386574 · doi:10.46697/001c.125512

How Political Risk Influences FDI Destinations for MNEs: Evidence from Statistical Analyses, Interviews and a Survey Experiment on Investor–State Dispute Settlement

2024· article· en· W4404386574 on OpenAlex
Stefano Burzo

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

VenueAIB Insights · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDestinationsSettlement (finance)PoliticsForeign direct investmentState (computer science)Statistical evidencePolitical riskStatistical analysisSurvey data collectionStatistical surveyInvestor-state dispute settlementBusinessSurvey researchEconomic geographyInternational economicsPolitical scienceEconomicsEconometricsFinanceLawStatisticsBusiness administration

Abstract

fetched live from OpenAlex

Foreign direct investment involves substantial and long-term investments by foreign companies, which have sometimes been expropriated by national governments. Over the past 35 years, international investment treaties have enabled private companies to sue governments at international tribunals for alleged treaty violations, a shift from traditional state-to-state judicial actions. This dissertation explores how such disputes impact foreign direct investment destinations through statistical analyses and interviews. Additionally, a survey experiment in the U.S. empirically examines factors shaping public preferences for government behavior in these disputes. The findings enhance the existing literature and contribute to policy discussions on redefining the international investment regime.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.135
GPT teacher head0.360
Teacher spread0.224 · 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