Negotiating Work-Family Transitions: Reverse Family Migration among Second-Generation Hong Kong Mothers
Bibliographic record
Abstract
Gendered and generational understandings of circular migration are scant in studies of Chinese family migration. Filling this gap, this paper draws on in-depth interviews with twenty-six returnee families to examine the work–family transitions of previously employed, overseas-educated mothers who have re-migrated from Hong Kong to Canada, Australia, the United States, or the United Kingdom. These overseas-educated returnee mothers possess transnational backgrounds that differentiate them from most first-generation immigrant mothers. This paper shows that, despite this distinction, reverse migration leads to compromised careers and domestication for these women, although they accept, and in some cases embrace, such compromises. This study elucidates how both husbands and wives in these families justify women’s post-migration changes in their work and caregiving roles. It argues that beyond economic rationalization, interrelated gender, cultural, transnational, and family lifestyle dimensions distinctively impact how second-generation returnee mothers negotiate work–family transitions. This paper offers new insights involving generational and gendered dimensions into the study of Chinese family migration. It also widens the discussion of the impact of family migration on skilled immigrant women in transnational circuits beyond its focus on the lives of first-generation skilled immigrant women.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".