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INTERNATIONAL COOPERATION BETWEEN RUSSIA AND VIETNAM: FORMS, SCALES AND PROSPECTS IN THE MIGRATION SPHERE

2020· article· en· W4367726757 on OpenAlexaboutno aff
Artem S. Lukyanets, Roman V. Manshin, Anastasia S. Maksimova

Bibliographic record

VenueToday and Tomorrow of Russian Economy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Political and Economic Relations
Canadian institutionsnot available
Fundersnot available
KeywordsVietnamesePopulationScope (computer science)Quarter (Canadian coin)Investment (military)CommodityVietnam WarEast AsiaPolitical scienceHuman migrationGeographyDevelopment economicsEconomicsInternational economicsChinaPoliticsMarket economySociologyDemography

Abstract

fetched live from OpenAlex

The article reveals the modern features of international cooperation between Russia and Vietnam. The characteristic of commodity circulation between countries is given. It has been established that in mutual trade in Russia it has a stable negative balance. At the same time, in 2019, Vietnam ranked 14th in terms of imports and for the year this indicator increased by 4.4%. In the first quarter of 2020, imports of goods and services from Vietnam were almost 5 times higher than exports to Vietnam. The analysis of data on the volume of investments showed that in recent years, the investment activity of Vietnamese investors in the direction of Russia has remained at an extremely low level.The scope and factors of population migration from Vietnam to Russia were also analyzed. Particular attention is paid to attracting and pushing factors for Vietnamese migrants. The characteristic is given to the main migration flows — labor and educational. The analysis of Vietnamese migration in Russia showed that it occupies a very modest place in the migration attitudes of Vietnamese. One of the reasons for this is the insufficiently active migration policy in Russia towards Vietnam, the transformation of which will contribute to the socio-economic development of strategically important regions of the country, such as the Far East.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score0.177

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.256
Teacher spread0.239 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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