Exploring trust in fellow townsmen among migrants and return migrants: testing selection and integration/reintegration hypotheses
Bibliographic record
Abstract
This study examines the factors influencing internal migrants’ trust in fellow townsmen in the context of migration and return migration within China. Using data from the China Labor-force Dynamics Survey (CLDS) 2016, it proposes and tests selection and integration/reintegration hypotheses to analyse how selection processes in migration and return migration, as well as integration and reintegration processes, impact trust in fellow townsmen. The findings reveal that migration and return migration significantly alter trust levels in fellow townsmen: rural-to-urban migrants exhibit lower trust compared to rural non-migrants, while rural return migrants experience a partial convergence or restoration of trust upon their return. Migration selection largely explains changes in trust, whereas return migration selection has little impact. Additionally, trust dynamics are shaped by integration and reintegration processes, with social integration and reintegration playing crucial roles in influencing trust in fellow townsmen among rural-to-urban migrants and rural return migrants, respectively. Larger institutional factors primarily influence rural-to-urban migrants’ trust, highlighting the asymmetric impact of institutional contexts on integration versus reintegration. This study contributes to the literature on migration and trust by emphasising the role of selection, integration, and reintegration processes in shaping trust in fellow townsmen among migrants and return migrants 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".