Intensification of traditional mothering ideals in migration contexts: low-income Mainland Chinese cross-border mothers in Hong Kong
Bibliographic record
Abstract
Research on migration and mothering has primarily focused on how migration reframes motherhood. Mothers are expected to be the primary caregivers and prioritise their children’s wellbeing over their own. However, transnational mothers face tensions between this conventional ideal and migration realities. Most research has concentrated on the strategies of migrating mothers in resolving these tensions. This study, however, reveals that not all migrant mothers could renegotiate ‘good mothering’ to counter traditional ideals. The authors conducted individual in-depth interviews with 26 low-income cross-border Chinese mothers coming from Mainland China to Hong Kong. The study found that they reinforced and intensified traditional mothering ideals and struggled to meet such moral expectations because their visa status did not allow employment and access to Hong Kong’s social security system. Mothers subordinated their own needs and wellbeing to prioritise their children’s. They blamed themselves for not being able to care for their family in Mainland China. They also needed to rebuild social networks in Hong Kong centred around their children’s needs. The findings suggest that migrant mothers’ agency to redefine motherhood in a transnational context is limited by the intersection of their social class and citizenship status.
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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) | 0.001 | 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".