Localising social work practice for migrant workers’ children in China: An action research learning from other countries
Bibliographic record
Abstract
Abstract While literature abounds in studying the phenomenon of migrant workers in China and offering policy suggestions from a macro perspective, this article proposes an inspiration for clinical social workers by developing a comprehensive case management model which aims at improving the well-being of migrant workers’ children. It is argued that lessons could be learned from theories, methods and strategies to address immigration-related issues in the USA, Canada and some European countries, because migration patterns of China’s migrant workers and international immigrants are similar. An action research approach is adopted. Unstructured interviews are conducted with clinical social workers, heads of social work organisations and schoolteachers. Based on the findings, a draft version of the case management model is constructed by borrowing immigrant social work methods selectively. As licensed social workers, the authors offer case management services to migrant workers’ children and families under a social work agency’s supervision in two urban–suburban-integrated schools. In the service process, the model is finished by modifications and localisation. The revised version of the model is elaborated in four steps. In each step, specific methods for migrant workers’ children and families are presented with related case examples attached.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".