Migrants’ (m)Othering Under a Neoliberal Gaze: An Ethnographic Inquiry on Multicultural Family Services in South Korea
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".