Designing new Korean mothers, daughters-in-law, and wives: an analysis of Korean textbooks for newly arrived marriage migrants in South Korea
Bibliographic record
Abstract
Abstract Textbooks are sociocultural materials, reflecting political decisions, educational beliefs and priorities, cultural realities and language policies. As part of a larger ethnographic study which investigated the multilingual socialization of foreign wives in South Korea, I present the nature and extent of the gender-making process through an analysis of Korean textbooks for recently arrived female marriage migrants, which provides an understanding of the extent to which gender and race are ingrained in shaping linguistic nationalism in globalized times. I first introduce a four-stage life cycle designed by the South Korean government and situate Korean textbook series called Korean Language Learning With International Marriage Migrant Women as an intervention used early in the settlement period for foreign mothers. Then, I analyze the textual and multimodal representation of family identities taken from six textbook series, focusing on lessons, dialogues, and characters that are presented. The results of the study demonstrate how the state presents its attempts to transform foreign wives into a new type of ‘wise mother good wife’ in the globalized, multilingual world. I demonstrate the ways in which state-driven gender identity production is not simply (re)producing the gender divide but also aligned with nation-making processes that are facing challenges in these globalized times.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".