Social, Cultural, and Civic Reintegration of Returning Rural Migrants in China: A Multidimensional Perspective
Bibliographic record
Abstract
Understanding the reintegration of returning rural migrants in China is crucial due to the large scale of return migration and its associated challenges. While existing research has largely focused on economic reintegration, this study broadens the scope to include social, cultural, and civic dimensions. Using data from the China Labor-force Dynamics Survey (CLDS) 2016 and employing multilevel ordered logistic regression, the research uncovers the following key patterns: (i) Determinants differ largely across dimensions; (ii) The roles of the same determinants can also differ significantly across dimensions; and (iii) There are significant community-level variations across dimensions. The findings emphasize that success in one dimension, such as economic reintegration, does not necessarily translate into success in others. Moreover, complex interconnections between dimensions reveal positive, negative, and non-linear relationships, underscoring the multidimensional nature of reintegration. These insights highlight the importance of considering multiple dimensions to fully understand the reintegration processes of returning migrants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".