Transnationalism and migrant entrepreneurship: a case study of self-employed foreigners in Hangzhou, China
Bibliographic record
Abstract
International migrant entrepreneurship in China has expanded and grown. Yet, studies on this phenomenon in China have been limited. We aim to contribute to this literature by exploring international migrant entrepreneurship for various categories of international migrants in a single setting and suggesting a more comprehensive picture of migrant entrepreneurship in China. We rely on the transnationalism framework and conduct various regression analyses using our unique dataset—Survey of Foreigner Residents in China (SFRC) from 2018-2019 to study both factors in China and factors from migrants’ home country, as well as the interactions between these two types of factors, in determining migrants’ likelihood of being an entrepreneur. Our results suggest that social networks and language skills in China, the ownership of the assets in migrants’ home country, and the economy and culture of their home country can significantly shape migrants’ likelihood to become an entrepreneur. These findings show the major role of factors from migrants’ home country, highlighting the importance of using the transnationalism framework to study migrant entrepreneurship. We also find evidence of only a few interaction effects between factors in China and factors from migrants’ home country, suggesting that they are weakly dependent on each other in influencing migrant entrepreneurship in China.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".