Stigmatization Experienced by Rural-to-Urban Migrant Workers in China: Findings from a Qualitative Study
Bibliographic record
Abstract
Global literature has suggested a potential negative impact of social stigma on both physical and mental health among those who are being stigmatized. However, limited data are available regarding the form of stigma and stigmatization against rural-to-urban migrant workers in developing countries, including China. This study, employing qualitative data collected from focus group discussions and in-depth individual interviews with rural-to-urban migrants in Beijing, China, was designed to understand the forms and context of stigmatization against rural migrant workers. The data in the current study show that rural-to-urban migrant workers in China had experienced various forms of stigmatization including labelling, stereotyping, separation, status loss and discrimination. Stigmatization occurred through different contexts of migrant workers' lives in urban destinations, including employment seeking, workplace benefits, and access to health and other public services. The current study is a necessary first step to assess the potential impact of stigmatization on both the physical and psychological well-being of rural-to-urban migrant workers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".