Bibliographic record
Abstract
Abstract Since China's economic opening and reforms in 1978, the country has broadened and deepened its exchanges and relations with other countries. This has contributed to the increase in the scale of international migration of highly skilled Chinese abroad. The impact of the migration of highly skilled Chinese on China and the relevant nations particularly deserve attention and study. Following the earlier migration flows mainly to the United States, Japan, Canada, Australia, and New Zealand, the migration of highly skilled Chinese to Europe has become a notable new trend. Currently, the flow of international migration of highly skilled Chinese personnel is mainly oriented toward Europe and the United States. While studying abroad has been the main form of migration of the skilled, this has now been joined by the migration of technical and professional staff, and the trend is increasing. The main country of destination for Chinese students is the United States, which absorbs more than half of the total, while Australia and Canada receive the largest number of skilled Chinese manpower. The United States also receives a large number of Chinese technical personnel, but its proportion has declined, while the flow to Europe has sharply increased. This development may be attributed to the global expansion of economic, scientific and technological, as well as cultural and educational exchanges and cooperation. But it is also the result of an increase in the educational investment made by the Chinese people following the continuous increase in China's economic strength and the population's personal income. Of greater importance are the gaps between China and Europe at the scientific, technological, and educational levels and the research and marketing environment. The intervening changes in labour market and immigration policies in European and American countries accelerate the trend further. For all of these and other reasons, the spatial distribution of Chinese students will become more balanced and play a positive role in the promotion of mutually beneficial exchanges between China and other countries.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".