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Record W4413094368 · doi:10.1186/s12992-025-01134-3

Beneath the rhetoric of global justice: Reinforcement of global hegemonic governmentality by South Korea’s Global Vaccine Hub Project

2025· article· en· W4413094368 on OpenAlexaff
Jimin Gim, Jiwon Park, Sun Kim

Bibliographic record

VenueGlobalization and Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsUniversity of Toronto
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsGovernmentalityGlobal governanceGovernment (linguistics)Political sciencePublic administrationSociologyDiplomacyPolitical economyEconomic growthPublic relationsEconomicsLawPolitics

Abstract

fetched live from OpenAlex

BACKGROUND: During the coronavirus disease 2019 pandemic, the South Korean government initiated the Global Vaccine Hub Project (GVHP) purportedly to address global vaccine inequality. This study analyzes the strategies and underlying epistemology of GVHP through the perspective of global governmentality. Critical Discourse Study (CDS) approaches were used to identify governmental technologies and explain how their embedded knowledge is related to power relations. RESULTS: The findings reveal that GVHP merely pursues national interests by implementing governmental technologies, such as calculative practice, support to private companies, patent protection and circumvention, and pursuing vaccine diplomacy. The South Korean government considered the pandemic an economic and diplomatic opportunity to become an advanced country. The governmental strategies resulted in the depoliticization of vaccines and facilitated the government's opposition to other alternatives, such as an intellectual property waiver at the World Trade Organization level. CONCLUSION: This study argues that the failure of global pandemic governance does not imply the failure of global governmentality; rather, the success of neoliberal global governmentality made global solidarity challenging.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.377
Teacher spread0.351 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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