Multicultural at the Meso-Level: Governing Diversity within the Family in South Korea
Bibliographic record
Abstract
Departing from extant studies that largely focus on gender roles, norm diffusion, or ethnonationalism, this paper highlights policy siting as one understudied factor in determining why and when states manage cultural diversity. Using the case of South Korea's family-centred multicultural policy, the paper contributes to the growing body of literature on comparative policymaking, multiculturalism, and multi-level citizenship by foregrounding the processes by which governing elites target specific meso-level social institutions as privileged sites of diversity governance. The paper draws on immersive field research conducted between 2017 and 2023 to introduce and analyze the concept of familial multiculturalism to explain how the state locates diversity governance mainly within the family and between family and broader society. Siting diversity governance in powerful meso-level institutions like the nuclear family in shaping state multiculturalism is not unique to Korea. Rather, the paper contends that these institutions play a significant role in cultural management endeavours worldwide. While the content of a multicultural site depends on history and national context, states worldwide seek to mitigate social friction and political backlash by targeting certain intercultural relations and negating or delegitimizing others. The paper concludes with a discussion of the contemporary political ramifications of Korea's multiculturalism and prospects for future broadening and deepening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".