Enhancing Social Support for Migrant Families: A Case Study of Community Services in a Shanghai Urban Village and Implications for Intervention
Bibliographic record
Abstract
Recent years have witnessed the growing emphasis of the Chinese central government to develop community services as a method of building communities and strengthening social solidarity. With the increased involvement of multi‐generation households in China's internal rural‐to‐urban migration, however, little is known about what community services are available for migrant families. Nor do we know much about how such services can enhance social support for migrants, which is crucial for their psychological well‐being in managing the ongoing challenges that arise from migration and further integration into cities. This article presents a case study conducted in Shanghai where social services are emerging in a few urban villages. We begin with a brief background on China's rural‐to‐urban migration and the emergence of urban villages, followed by a discussion of community services and social support for Chinese migrant families. We then document existing services in an urban village to explore how they can influence migrant families' social support. Drawing on the perspective of service providers, we highlight the effects social work interventions can have on improving social support for migrant families. Finally, we propose an intervention framework based on multi‐dimensions of social support, emphasizing an integration of formal and informal social support through community services for migrant populations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".