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Record W4391590286 · doi:10.3917/eh.112.0098

Chinese-russian cooperation in the automobile field : experience and prospects 2000-2022

2023· article· fr· W4391590286 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEntreprises et histoire · 2023
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsFrancophone University Association
Fundersnot available
KeywordsField (mathematics)Political scienceBusiness

Abstract

fetched live from OpenAlex

Cet article aborde la question de la présence des constructeurs automobiles chinois sur le marché russe. Sont analysés le positionnement et les difficultés que rencontrent les constructeurs chinois lors de leur entrée sur le marché russe. L’auteur examine le rôle des sanctions occidentales vis-à-vis de l’industrie automobile russe, notamment la manière dont les sanctions influencent la position des constructeurs chinois, sachant qu’officiellement, la Chine n’a jamais soutenu les sanctions contre la Russie. Enfin, l’auteur se concentre sur les changements causés par la guerre en Ukraine, qui a débuté en 2022. Cet événement a provoqué des changements tectoniques du marché automobile russe. Le départ des constructeurs occidentaux ouvre des perspectives importantes pour les constructeurs chinois dans le cas où les constructeurs occidentaux ne reviendraient pas sur le marché automobile russe dans un avenir proche.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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