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Record W4224211799 · doi:10.3390/jrfm15040187

To Trust or Not to Trust? COVID-19 Facemasks in China–Europe Relations: Lessons from France and the United Kingdom

2022· article· en· W4224211799 on OpenAlexvenueno aff
Émilie Tran, Yu-chin Tseng

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsChinaMainland ChinaDistrustDiplomacyPopulationPolitical scienceSociologyLawPolitics

Abstract

fetched live from OpenAlex

At the crossroads of sociology and international relations, this interdisciplinary and comparative research article explores how the COVID-19 outbreak has impacted China–Europe relations. Unfolding the critical moments of the COVID-19 outbreak, this article characterizes the evolution of China–Europe relations with regard to the facemask. This simple object of self-protection against the coronavirus strikingly became a source of contention between peoples and states. In the face of this situation, we argue that the facemask is the prism through which to illustrate (1) the transnational links between China and its overseas population, (2) the changing social perceptions of China and Chinese-looking people in European societies, and (3) the advent of China’s health diplomacy and its reception in Europe. Comparing two European settings—France and the United Kingdom (UK)—the common denominator appears to be the reduced trust, if not outright distrust, between individuals and communities in the French and British contexts, and in Sino–French and Sino–British relations at the transnational level. Combining critical juncture theory and (dis)trust in international relations as our analytical framework, this article examines how the facemask became a politicized object, both between states and between Mainland China and its overseas population, as the epidemic unfolded throughout Europe. Adopting a qualitative approach, our dataset comprises the analysis of official speeches and statements; press releases; traditional and social media content (especially through hashtags such as #JeNeSuisPasUnVirus, #IAmNotAVirus, #CoronaRacism, etc.); and interviews with Chinese, French, and British community members.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.100
GPT teacher head0.439
Teacher spread0.338 · 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.

Study designNot applicable
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

Citations4
Published2022
Admission routes1
Has abstractyes

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