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Labor and Educational Migration оf Mongolian Residents to South Korea and Canada

2024· article· en· W4392137202 on OpenAlexaboutno aff
A. V. Bredikhin

Bibliographic record

VenueHumanities and Social Sciences Bulletin of the Financial University · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Ethnic Minorities and Relations
Canadian institutionsnot available
Fundersnot available
KeywordsChinaEast AsiaGeographyEconomic growthEconomyPolitical scienceBusinessEconomics

Abstract

fetched live from OpenAlex

At the beginning of the 21st century. Northeast Asia has become one of the centers of economic development and growth. There are many reasons for this. One of the main ones is the rapid growth of the economies of China, Japan, South Korea, and Taiwan. Thus, China has become the largest economic force in the region. Japan and South Korea have powerful industrial and technology companies that successfully compete in the global market. In addition, Northeast Asia has a high level of education and science, so China, Japan and South Korea are investing in scientific research, which attracts highly qualified specialists to these countries. Across the Pacific Ocean from Asia, on the North American continent, Canada has a strong economy based on industries such as natural resources, manufacturing, finance and innovation. As part of this study, the author examined the reasons for the migration of Mongolian residents to South Korea and Canada.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.999

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 studies0.0030.001
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 head0.019
GPT teacher head0.229
Teacher spread0.209 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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