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Record W2339060512 · doi:10.1111/imig.12246

Immigration Policies and the Factors of Migration from Developing Countries to South Korea: An Empirical Analysis

2016· article· en· W2339060512 on OpenAlexaboutno aff
Ador R. Torneo

Bibliographic record

VenueInternational Migration · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationUnemploymentDemographic economicsInternal migrationGovernment (linguistics)Human migrationDeveloping countryPoliticsPopulationImmigration policyDevelopment economicsEthnic groupPolitical scienceEconomicsEconomic growthDemographySociology

Abstract

fetched live from OpenAlex

Abstract This study examines the impacts of immigration policies adopted by the Korean government, vis‐a‐vis other economic, social, demographic, and political factors, on labour migration from developing countries to South Korea using a modified gravity model. The model is extended to marriage‐related migrants to gain insights on marriage migration. The positive results in three out of the five immigration policies examined affirm that liberal policies are associated with increased migration, especially for preferred groups like ethnic Koreans, marriage migrants, and professionals. The positive effects of “push” factors such as population, unemployment, and inflation are generally similar to their effects on migration to the US , Canada, Germany, and the UK despite its more rapid transition from a migrant‐sending into a migrant‐receiving country. Political terror's non‐significance may be due to South Korea's limited asylum policy. Finally, the results of the extended model imply that marriage migration share plenty of similarities with labour migration.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.990

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 communication0.0000.001
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 head0.023
GPT teacher head0.333
Teacher spread0.310 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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