Redefining Multicultural Terminology in Korea and the Paradigm of Coexistence Diversity
Bibliographic record
Abstract
In the 21st century, South Korean society is facing a critical challenge in redefining its social identity amid complex transformations, including ultra-low birth rates, rapid population decline, an increase in immigrants, digital transformation, and the adoption of artificial intelligence (AI) technologies. In particular, demographic shifts—marked by a declining birth rate and an increasing immigrant population—underscore the urgent need for South Korea to establish a multicultural coexistence model. However, existing multicultural terminology and policies, such as multicultural families and multicultural students, maintain a frame centered on specific groups, which has historically functioned not to foster social integration, but rather to reinforce distinctions and stigmatization. This study analyzes the issues inherent in South Korea’s multicultural terminology by drawing on the philosophical discussions of Foucault, Baudrillard, Derrida, Geertz and Said. Furthermore, it examines multicultural policies in the United States, France, and Canada to propose the concept of Coexistence Diversity. This study also emphasizes the necessity of expanding the concept of multiculturalism in response to changes in the educational environment, including digital ethics and the adoption of AI-driven textbooks. Beyond existing multicultural policies, South Korea must move towards reconstructing its future social identity. This study presents the concept of a new social identity co-formed by both immigrants and native citizens. Ultimately, the Coexistence Diversity model is considered essential for South Korea’s multicultural policies and education as a new paradigm for social integration.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".