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Record W4410280936 · doi:10.1177/08861099251340628

Migrants’ (m)Othering Under a Neoliberal Gaze: An Ethnographic Inquiry on Multicultural Family Services in South Korea

2025· article· en· W4410280936 on OpenAlexaff
Eunjung Lee

Bibliographic record

VenueAffilia · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMulticulturalismEthnographyGazeSociologyGender studiesPsychologyPedagogyPsychoanalysisAnthropology

Abstract

fetched live from OpenAlex

This article takes South Korea (hereafter Korea) as the location of a proposed case study on migrant mothering in a neoliberal state . It attempts to elaborate how gendered nation-building initiatives deploy the concept of multiculturalism while selectively targeting female marriage migrants (FMMs), and how this very process reifies gender, ethnicity, and class inequity in everyday institutional practice. Guided by feminist scholars’ work on the feminization of migration and intensive mothering, and using ethnographic research as a method, this study examined the institutional reification of neoliberal multicultural policy . It explored how gendered neoliberal nation-building is translated into mundane practices at a multicultural family support center. Findings include that patriarchy and cultural paternalism pervaded both state policies and everyday practice by regulating service eligibility and constructing FMMs as victims in need of paternalistic supports. Meanwhile, migrant mothers remained under multiple levels of surveillance to receive services at multicultural family support centers and were pressured to perform ‘good mothering’ through a preoccupation with their children's language education. As a result, FMMs were otherized and inferiorized in terms of gender, class, ethnicity, and social status while their multicultural children were constructed as social capital to be invested in for Korea's economic future.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.984

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)0.0000.000

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 head0.039
GPT teacher head0.317
Teacher spread0.278 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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