Live like mosquitoes: <i>Hukou</i> , rural–urban disparity, and depression
Bibliographic record
Abstract
Although there has been a longstanding curiosity about the socio-political consequences of China’s remarkable urban–rural divide, we have yet to understand the divide’s possible influence on mental health. Using data from the 2016 wave of the China Labor-force Dynamics Survey (CLDS), we find that depressive symptoms of both rural–urban migrants and rural residents are significantly higher than those of urban residents. Consistent with the fundamental-causes-of-disease and stress-exposure perspectives, results from zero-inflated negative binomial regression suggest that such differences in depressive symptoms can be attributed to socioeconomic status and proximate stressors such as unemployment, living alone, and the unaffordability of medical services. In particular, the rural–urban difference in depressive symptoms is explained away by educational attainment. A further investigation using spline Poisson regression suggests that the protective effects of the period of middle school, which vary substantially across demographic groups, are especially relevant to the rural–urban disparity in depression. We argue that hukou is a fundamental cause of disease in China and mental health is an important yet understudied area where China’s salient urban–rural inequality strikes.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".