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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".