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Record W2993232365 · doi:10.1093/cjip/poz016

Geopolitics, Nationalism, and Foreign Direct Investment: Perceptions of the China Threat and American Public Attitudes toward Chinese FDI

2019· article· en· W2993232365 on OpenAlexaff
Ka Zeng, Xiaojun Li

Bibliographic record

VenueThe Chinese Journal of International Politics · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsForeign direct investmentChinaGeopoliticsNationalismPolitical scienceNational securityForeign policyPolitical economyAffect (linguistics)PoliticsDevelopment economicsEconomicsSociology

Abstract

fetched live from OpenAlex

Abstract The rapid increase in recent years of Chinese outbound foreign direct investment (FDI) has prompted growing scholarly interest in its economic and political implications for host countries. However, relatively little attention has been paid to how concerns over the rise of China may shape public attitudes towards such investment. This article tests the link between threat perception and preferences for FDI in the United States. We argue that, due to heightened geopolitical concerns and nationalism, perceptions of the China threat negatively affect how the American public views the impact of incoming Chinese FDI. Using a survey experiment, we show that respondents are indeed less likely to support Chinese FDI when primed with information that highlights the security and economic threats posed by China than when they receive no such priming. Furthermore, causal mediation analyses reveal that the treatment effects of security and economic threats are mediated by respondents’ concerns about the challenges that Chinese FDI poses to national security as well as to American economy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.423

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 communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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 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

Citations41
Published2019
Admission routes1
Has abstractyes

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