Migrants’ reference group selection: insights from the multidimensional assimilation framework
Bibliographic record
Abstract
This study explores how cultural, identity, economic, and structural assimilation shape rural-to-urban migrants’ choice of reference group in China. Understanding reference group selection is important because it can influence economic outcomes, subjective well-being, and intergroup attitudes. Using data from the Chinese Household Income Project (CHIP) 2013, we apply multinomial logistic regression to analyze reference group selection. The results indicate that cultural assimilation measured by migration distance and duration does not significantly predict reference group choice, suggesting that cultural assimilation is insufficient to explain migrants’ social comparisons in recent China. In contrast, identity and economic assimilation play key roles. Particularly, migrants who intend to settle permanently in urban areas and those with higher education and financial statuses are more likely to compare themselves with urban residents. Structural assimilation produces mixed results; institutional barriers such as hukou and insurance statuses show little effect, but living in supercities influences reference group selection in unexpected ways. These findings highlight the multidimensionality of migrants’ reference group choices and suggest that policymakers should prioritize urban inclusion and economic empowerment initiatives to shape migrants’ reference group choices.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".