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Record W2097636073 · doi:10.7202/1015121ar

La Chine en Arctique

2013· article· fr· W2097636073 on OpenAlexaffvenue
Olga Alexeeva, Frédèric Lasserre

Bibliographic record

VenueÉtudes internationales · 2013
Typearticle
Languagefr
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsUniversité LavalUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Depuis quelques années, de nombreux articles de presse et textes d’analystes prédisent un regain de tension dans l’Arctique, dans ce qui serait une course à l’appropriation des espaces maritimes. Dans cette course, la Chine chercherait à se poser en acteur incontournable. Nombre d’analyses mettent l’accent sur les ambitieux projets chinois dans la région et sur le potentiel déstabilisateur de ces ambitions. En réalité, si le gouvernement chinois semble préciser, depuis peu, une politique arctique, rien ne laisse entendre que la Chine va contester les revendications des pays riverains ou chercher à imposer un coup de force pour s’emparer des ressources supposées de la région.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.068
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.022
GPT teacher head0.325
Teacher spread0.303 · 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

Citations5
Published2013
Admission routes2
Has abstractyes

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