Contesting Boundaries and Navigating Identities: Second-Generation Adult Children from Cross-Border Marriages in Taiwan
Bibliographic record
Abstract
Scholars who study the children of immigrants in North America and Western Europe have developed several paradigms to analyze the second generation's ethnicity. The major ones are options , capital , and boundary-making . This article contributes to this literature by exploring the emerging formation of second-generation identity in East Asia. Although the region is known for its self-perceived racial and ethnic homogeneity, an influx of marital immigrants and their bicultural children has transformed its demographic landscape. Through in-depth interviews with 57 adult children from cross-border marriages in Taiwan, this article examines their strategies for identity management under the typologies of majority identity, biculturalism, rescaling, and differentiation . Because of changing receiving contexts as a result of the state's policy of geopolitical multiculturalism, a bicultural identity has increasingly become a likely option for children of Southeast Asian mothers. Ethnic dividends are mostly available for university students with academic capital, but they are not equally accessible to children of PRC-Chinese immigrants. While the boundaries dividing “Taiwanese,” as the mainstream national group, from immigrants and their offspring have shifted and softened in recent years, second-generation children are obliged to become national subjects whose ethnic identity does not conflict with national loyalty and whose patriotic duty is to convert their ethnic capital into transnational networks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".