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Record W4323345663 · doi:10.7202/1097237ar

Le soft power chinois : entre politiques volontaristes et succès limités

2023· article· fr· W4323345663 on OpenAlexaffvenue
Clément Broche

Bibliographic record

VenuePolitique et Sociétés · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

En février 2022, lors des Jeux olympiques d’hiver de Beijing 2022, la Chine est devenue le centre d’intérêt de la planète durant les deux semaines de cette messe sportive internationale. À cette occasion, le géant asiatique a eu la possibilité de faire la démonstration de son soft power. Élément désormais essentiel à l’expression de la puissance des grandes nations, celui-ci illustre la capacité des États à séduire par leur modèle. Concept défini par Joseph Nye en 1990, le présent article se propose de revenir sur la mise en oeuvre du soft power en Chine et d’en évaluer l’efficacité à travers une étude de cas des différents efforts déployés par Pékin en ce sens.

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)0.004
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.027
Scholarly communication0.0070.007
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.087
GPT teacher head0.413
Teacher spread0.326 · 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
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
Published2023
Admission routes2
Has abstractyes

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