The Comings of the Foreign-born for PhD and Postdoctoral Study: A Sixteen Country Perspective
Bibliographic record
Abstract
We analyze the decisions of foreign-born PhD and postdoctoral trainees in four fields of science to come to the United States vs. another country for study. Data are drawn from the GlobSci survey conducted in 2011 of research active scientists residing in sixteen countries. We find that in both cases the United States is the most common destination country. Individuals come to the U.S. to study because of the prestige of the program and/or career prospects. For recent trainees, the availability of financial assistance also plays an important role. When we expand the data to a longer time span, we find that the attractiveness of the U.S. compared to other countries for the PhD declines for those who received their degree after 2000; for postdoctoral training it has declined since 1990. Factors that discourage the foreign born from getting a PhD in the U.S. vs. another country are the perceived U.S. life style and the availability of fewer exchange programs, compared to those in other countries, especially in the EU. The relative attractiveness of fringe benefits discourages the foreign born from taking a postdoc position in the U.S. The countries that have been nibbling at the U.S. share include Australia, Germany, Great Britain, Japan and Switzerland. France has gained appeal in attracting postdocs, but not in attracting PhD students. Canada has made gains in neither.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".