Bibliographic record
Abstract
Traditional approach to the issue of “brain drain” and “brain gain” focuses on outflow and inflow of migration of academics and professionals between countries of origins and destinations. It is suggested that, in the international labor market, the developing countries have experienced the problem of brain drain while the developed countries have benefited from brain gain in the process of globalization and international mobility of talent. From this perspective, “brain drain” or “brain gain” is primarily measured by the number of talented people who have “moved in” or “moved out” of a country, but not the extent to which the “brain” has been utilized. This study redefines the notion of “brain drain” by focusing on the actual utilization of professional talents. Previous research findings show that despite attractive Canadian immigration policy and the increasing number of professional immigrants, Canada as a developed country has the problem of “brain waste” due to its systemic barriers such as the devaluation of foreign credentials and non-recognition of foreign work experience for professional Chinese immigrants. At the same time, China as a developing country has benefited from contributions made by highly educated professionals/students returning to their home country through its attractive and rewarding opportunities for those who have attained knowledge and skills from overseas. China has become a model of “brain gain” for developing countries by implementing a series of open and favorable policies to attract top-notch overseas Chinese and foreign talents to help promote the economic development and global competitiveness of the nation.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".