Mobility and International Collaboration: Case of the Mexican Scientific Diaspora
Bibliographic record
Abstract
We use a data set of Mexican researchers working abroad that are included in the Mexican National System of Researchers (SNI). Our diaspora sample includes 479 researchers, most of them holding postdoctoral positions in mainly seven countries: USA, Great Britain, Germany, France, Spain, Canada and Brazil. Their research output and impact is explored in order to determine their patterns of production, mobility and scientific collaboration as compared with previous studies of the SNI researchers in the periods 1991-2001 and 2003-2009. Our findings confirm that mobility has a strong impact on their international scientific collaboration. We found no substantial influence among the researchers that got their PhD degrees abroad from those trained in Mexican universities. There are significant differences among the areas of knowledge studied: biological sciences, physics and engineering have better production and impact rates than mathematics, geosciences, medicine, agrosciences, chemistry, social sciences and humanities. We found a slight gender difference in research production but Mexican female scientists are underrepresented in our diaspora sample. These findings would have policy implications for the recently established program that will open new academic positions for young Mexican scientists.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".