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Record W4384822058 · doi:10.1080/14631369.2023.2236991

Marriage migrant women’s friendship formation in South Korea

2023· article· en· W4384822058 on OpenAlexaff
Gowoon Jung, Eugena Kwon, H. Sohn Hansem Sohn

Bibliographic record

VenueAsian Ethnicity · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsTrent University
Fundersnot available
KeywordsFriendshipEthnic groupGender studiesImmigrationSociologyPolitical scienceSocial psychologyPsychology

Abstract

fetched live from OpenAlex

Despite the substantial research on marriage migrant women in Korea documenting the challenges they experience as they adapt to Korean society, little is known about their relationships outside of families. To fill the gap, this study examines how marriage migrant women make friends, especially focusing on the concepts of intra-ethnic and inter-ethnic friendship. Our findings suggest that marriage migrant women are likely to form close relationships with people from the same country of origin as a buffer against adaptation stress, which indicates strong intra-ethnic friendship. Regarding inter-ethnic friendship, women tend to prefer friends who are from Korea to those from other East Asian countries, which indicates their desire of learning Korean language and ethnic culture. Overall, this study contributes to the understanding of how women form a variety of friendships and how marriage migrant women’s co-ethnic friends as well as Korean friends assist in their transition to Korea.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.304
Teacher spread0.274 · 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 designQualitative
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

Citations3
Published2023
Admission routes1
Has abstractyes

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