Foreign Born Scientists: Mobility Patterns for Sixteen Countries
Bibliographic record
Abstract
We report results from the first systematic study of the mobility of scientists engaged in research in a large number of countries. Data were collected from 17,182 respondents using a web-based survey of corresponding authors in 16 countries in four fields during 2011. We find considerable variation across countries, both in terms of immigration and emigration patterns. Switzerland has the largest percent of immigrant scientists working in country (56.7); Canada, and Australia trail by nine or more percent; the U.S. and Sweden by approximately eighteen percent. India has the lowest (0.8), followed closely by Italy and Japan. The most likely reason to come to a country for postdoctoral study or work is professional. Our survey methodology also allows us to study emigration patterns of individuals who were living in one of the 16 countries at age 18. Again, considerable variation exists by country. India heads the list with three in eight of those living in country when they were 18 out of country in 2011. The country with the lowest diaspora is Japan. Return rates also vary by country, with emigrants from Spain being most likely to return and those from India being least like to return. Regardless of country, the most likely reason respondents report for returning to one’s home country is family or personal.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".