Where Do the Brainy Italians Go
Bibliographic record
Abstract
This paper studies the major determinants that affect the country location of university-educated Italian scientists and researchers who live abroad in three alternative geographic areas: the USA/Canada, the UK and other EU countries. In our sample, 38% of the brainy Italians are in other EU countries, 37% in the USA and 25% in the UK. Ceteris paribus, both push and pull factors are important: having a PhD from outside Italy predicts settling in the UK, while having extra working experience from outside Italy predicts migration to other EU countries. Specialization in the fields of humanities, social sciences and health are strong determinants of migration to the UK. For the move to the USA, specialization in the humanities is a significant deterrent, while specialization in health is a positive deciding factor. Those who stay abroad for less than two years, or for two to four years are definitely more likely to go to the UK. Lack of funds in Italy constitutes a significant push to the USA.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".