“Get Rich and Get Going”: Understanding Chinese Lifestyle Migrants to Western Countries
Bibliographic record
Abstract
There is a tendency in migration studies to categorize all voluntary migrants without economic incentives as lifestyle migrants. As members of the Chinese middle class migrated to Western countries over the past two decades, after accumulating wealth in their homeland, they, too, were easily subsumed into the category of lifestyle migrants. This study argues that contemporary wealthy Chinese migrants cannot simply be classified as lifestyle migrants since affluent Chinese mainlanders are motivated to move abroad due to concerns about domestic political issues, aspirations for better education, social and natural environments, and a relatively more autonomous lifestyle in the West. The study employs sixty semi-structured interviews with affluent mainland Chinese migrants to Australia, Canada, the United Kingdom, and the United States since 2000. It contributes to the broader discussion of the classifications of wealthy migrants from rising global economic powers by suggesting that no single classification or category fully captures the complexities of their motivations and evolving characteristics. The findings also facilitate a deeper understanding of the complex migration phenomena involving wealthy individuals from rising global economic powers, which have so far received limited attention in the literature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".