Unveiling the Social Fabric: The Impact of China’s Rural Subsistence Allowance System on Community Isolation
Bibliographic record
Abstract
This study investigates welfare stigma and its underlying mechanisms using data from the China Family Panel Studies Project. By employing propensity score matching, the research examines how indicators such as neighborhood relationships and interactions with relatives reflect social isolation within rural households. The analysis reveals critical determinants affecting rural families’ access to subsistence allowance assistance and explores the resultant social isolation effects within China’s rural subsistence allowance system. Our findings indicate that several factors—including family income, housing conditions, employment status, age demographics, health status, and village characteristics (such as landform and population density)—significantly impact the likelihood of receiving subsistence allowances. Additionally, the study highlights that the rural subsistence allowance system contributes to diminished neighborhood cohesion and fewer interactions with relatives among beneficiaries. The research further identifies a pronounced targeting bias within the district targeting mechanism of the allowance program, which is corroborated through robustness testing. Overall, this study provides novel insights into the relationship between welfare stigma and social isolation, offering valuable empirical evidence and policy recommendations to enhance the effectiveness of rural subsistence allowance policies in 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".