Registered (un)belonging : negotiating South Korea's institutionalized boundaries of belonging for migrants
Bibliographic record
Abstract
South Korea, a country once mired in the myth of national identity based on hyultong (혈통, bloodline), in 2006 officially declared its efforts to invest in building a damunhwa sahoe (다문화사회, multi-ethnic and multicultural society). Yet, despite various efforts South Korea has not been able to avoid discrepancies between its migrant integration policy objectives and their outcomes. In this thesis, I propose that both a problem and a solution lie in the boundaries of belonging embedded in South Korea's mainstream policies. I build my conceptual framework through reviewing literature written in English and Korean by migration and policy scholars and build my analytical framework on comparative policy analysis. This is a primarily theoretical thesis that makes use of real-world citations when possible, aiming to be a building block for subsequent empirical studies. I highlight South Korea’s resident registration policy as an example that illustrates how the policy practice of “fringing” (as opposed to mainstreaming) migrant issues and integration has contributed to the integration gap in South Korea. I then suggest how South Korea's history in negotiating institutionalized gender boundaries to reform its family registration system can be used to evolve the exclusionary boundaries of belonging within its resident registration system and similar mainstream policies.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".