Comments by Zhao Chen, on Quest for Talents: Attraction and Retention of Highly Skilled Overseas Chinese in the United States and Canada
Bibliographic record
Abstract
Zhao Chen: This paper addresses a very important topic. Overseas Chinese is a highly representative group of international immigrants worldwide who have contributed significantly to the development of high-tech sectors in developed countries. High-level talents of overseas Chinese returnees are also crucial for developing countries since they bring to their home country advanced know-how of technology embedded with their human capital. As a result, both attraction and retention of highly skilled overseas Chinese are important factors that policymakers should know, as they would reshape the spatial allocation of human capital of high-level talents.It is also a timely discussion on the recent situation about overseas Chinese since the situation for Chinese individuals in North America has become increasingly challenging in recent years. Meanwhile, the Chinese government has become more and more eager to attract high-level talent from overseas. So, given this situation, it is quite interesting to discuss the attraction and retention of highly skilled overseas Chinese in developed countries such as the United States and Canada.The authors have conducted a survey of overseas talents at the micro-level based on two questionnaires. I have some concerns about the representativeness of the data. First, I have to say that the sample is quite limited, making it difficult to judge if the findings are robust or not. Additionally, as we can see from the paper, most of the returnees are quite satisfied with their career or income—one possible concern is whether the sampling method makes the sample biased toward those with a higher satisfaction level. For example, those who are not satisfied might be inactive in social interactions and, as a result, are less likely to be covered by the survey.Nevertheless, the data is unique and provides a comprehensive view of both push and pull factors of Chinese overseas’ returning home country. Thus, it is possible to evaluate various factors associated with the career and income satisfaction of Chinese returnees, such as talent policy, marriage, food, and so forth. However, although it is interesting to know various factors that relate to the satisfaction of Chinese returnees, I still suggest that in the empirical work, the authors have a clear focus on some main factors.I assume that most readers will be quite interested in the role of the talent policy in attracting Chinese overseas. However, “talent policy” is a subjective measure in this paper, which indicates whether the returnee thinks a talent attraction policy is one of the main reasons for settling and developing their career at the destination. I would suggest that, in future studies, the authors could think about collecting data measuring local talent attraction policies at the city level and investigate whether such policies are effective in Chinese returnees’ locational choice of their destination when they want to settle down from overseas to mainland China. Of course, that would be another paper.In sum, I would say that this is an interesting paper although the data is quite limited. I hope it will attract a lot of interest and further discussion as well as studies on this topic.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".