Housing characteristics and health in urban China: A comparative study of rural migrants and urban locals
Bibliographic record
Abstract
Abstract China's internal migration has produced a massive population of rural‐to‐urban migrants who face more structural and policy disadvantages in cities, compared with urban locals. The two social groups potentially differ both in their housing characteristics and in the health effects of these housing characteristics. These two differences are fundamentally distinct components that make up the overall impact of housing on health disparity between urban locals and rural‐to‐urban migrants. Using the 2017 China Migrants Dynamic Survey data, this study explores the differing connections of housing characteristics and health between the two groups in today's urban China. Overall, housing type and size have greater effects on the health of migrants, whereas housing instability has a greater impact on the health of urban locals. We utilize the Blinder–Oaxaca decomposition method to uncover to what extent the disparity in health between the two groups is due to the difference in their housing characteristics or the difference in the health effect of housing characteristics. In so doing, this study reveals two interrelated but distinct sources of housing‐induced health disparity between urban locals and migrants in urban China.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".