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Record W4385858720 · doi:10.22452/ijcs.vol14no1.3

Chinese Development Aid and Finance: Effective Soft Power Tool or Public Diplomacy Liability? A Spatial Study of Project Influence

2023· article· en· W4385858720 on OpenAlexaff
Benjamin Toettoe

Bibliographic record

VenueInternational Journal of China Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSoft powerPublic diplomacyChinaSpillover effectGovernment (linguistics)BusinessPublic opinionForeign direct investmentCompetition (biology)EconomicsPolitical scienceFinancePublic economicsPoliticsEconomic growthDiplomacy

Abstract

fetched live from OpenAlex

China has risen to become a significant donor of foreign aid and assistance in recent years. As with other major donors, such economic flows in the provenance of China are tied to the latterʼs strategic aims. Specifically, the use of economic outlays as a tool of soft power and public diplomacy has been identified as a key motivation in Chinese disbursement decisions. However, studies examining the empirical success of Chinese foreign aid and assistance in acting as such have, so far, come to diverging conclusions. This study offers an investigation of the spatially diffused effects of infrastructure project sites tied to Chinese funding sources on local public opinion in Ecuador, a country that offers an interesting case showcasing the effects of the unfolding great power competition on local public opinion. We find, that controlling for socio-economic and ideological individual characteristics holding the potential to affect survey respondentsʼ views on China, project influence is significantly and negatively correlated with levels of trust in the Chinese government. These results suggest that the frequently mediatized negative local spillover effects of Chinese-funded or financed infrastructure projects make it unlikely for Chinaʼs foreign aid and assistance to achieve its envisioned purpose of furthering the countryʼs soft power and positive image beyond its borders.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.319
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.024
GPT teacher head0.388
Teacher spread0.364 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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