Homeland engagement and host-society integration: A comparative study of new Chinese immigrants in the United States and Singapore
Bibliographic record
Abstract
This article addresses three main questions through a comparative study of new Chinese immigrants in the United States and Singapore: (1) How do contexts of emigration and reception affect the ways in which new immigrants are tied to their homeland? (2) How do diasporic communities help members engage with the homeland? (3) What effects does transnationalism have on host-society integration? We develop an institutional approach to analyze how the state is involved in the transnational fields and how diasporic organizations serve as a bridge between individual migrants and state actors in transnational practices and integration processes. We find that new Chinese immigrants maintain emotional and tangible ties with China even as they are oriented toward resettlement in the hostland and that their transnational practices are similar in form but vary in magnitude, depending not only on diasporic positionality in the host society but also on bi-national relations. We also find that those who actively engage themselves in the transnational fields tend to do so through diasporic organizations. Finally, we find that homeland engagement generally benefits integration into host societies. These findings suggest that social forces at the macro-level – the nation-state – and at the meso-level – diasporic communities – are intertwined to affect processes and outcomes of immigrant transnationalism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".