Bibliographic record
Abstract
This paper studies the major determinants that affect the country choice of the talented Italian scientists and researchers who have at least a bachelor’s from Italy and live abroad. There are three alternative country choices: the US/Canada, the UK, and other EU countries. On average, the brainy Italians exhibit a higher predicted probability to go to the US. Ceteris paribus, both push and pull factors are important. While having a Ph.D. from outside Italy predicts the UK choice, having extra working experience from outside Italy predicts migration to other EU countries. Those who stay abroad temporarily for two to four years are definitely more likely to go to the UK. Specialization in the fields of humanities, social sciences, and health are strong determinants of migration to the UK. For the move to the US, while the humanities area is a significant deterrent, health is a positive deciding factor. Lack of funds in Italy constitutes a significant push to the US.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".