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Record W6989933147

Commercial diplomacy and American foreign policy

2016· other· en· W6989933147 on OpenAlexfundno aff

Bibliographic record

VenueEconstor (Econstor) · 2016
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersInternational Development Research CentreJohn D. and Catherine T. MacArthur Foundation
KeywordsNucleofectionTSG101HyporeflexiaArticular cartilage damagePretextTubulopathy
DOInot available

Abstract

fetched live from OpenAlex

Throughout much of the 20th century, American diplomats had little incentive to invest time and effort in commercial diplomacy. Since 1990, however, commercial diplomacy has (re)emerged as a priority in American foreign policy, despite the fact that American businesses are increasingly empowered to act as independent agents in the global economy. This paper examines the rise of commercial objectives in contemporary American diplomacy. I argue that since the end of the Cold War, the historical tension between supporting American businesses abroad and pursuing a strategic foreign policy has evaporated; today, diplomatic interventions to support businesses abroad strengthen the American foreign policy objective of promoting investment climate reforms in developing countries. Specifically, interventions in investment disputes provide American diplomats with valuable private information on a given host state's commitment to liberal economic policies, and serve as focal points for discussions on the importance of a strong investment climate. This argument is supported by two case studies of American diplomatic (non)interventions in investment disputes in Ukraine and Liberia. The findings suggest a persistent role for diplomacy in the modern investment regime, despite the availability of investor-state arbitration as a mechanism for resolving investment disputes.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0080.002
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.282
Teacher spread0.270 · 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 designNot applicable
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

Citations1
Published2016
Admission routes1
Has abstractyes

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