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Record W2923997165 · doi:10.1080/19186444.2019.1578157

The political relationship between U.S. multinational corporations and the NAFTA investment chapter: the case of manufacturing multinationals

2019· article· en· W2923997165 on OpenAlexvenueno aff
Jesse Liss

Bibliographic record

VenueTransnational Corporation Review · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsMultinational corporationPoliticsForeign direct investmentInternational tradeBusinessInvestment (military)International economicsEconomicsPolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

Many trade policy researchers and stakeholders claim that U.S. multinational corporations (MNCs) have near fiat political power to write U.S. trade and investment law, which they use to profit from in international markets. This article focuses on the relationship between U.S. MNCs and U.S. investment treaties using the NAFTA investment chapter and U.S. manufacturing foreign direct investment (FDI) to Mexico as a case study. The case study tests a causal model in which market power begets political power in U.S. trade policy, and vice versa. Evidence is presented that the model held true until recent NAFTA renegotiations, which led to major revisions of the investment chapter. The case study illustrates that when it comes to writing the rules of the global economy, corporate power begets power, but that power can be successfully contested.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.003
Scholarly communication0.0070.003
Open science0.0000.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.108
GPT teacher head0.269
Teacher spread0.161 · 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 designObservational
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

Citations2
Published2019
Admission routes1
Has abstractno

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