An analysis of international mobility and research productivity in computer science
Bibliographic record
Abstract
Abstract In this article, we study the international mobility of researchers in the field of computer science (CS). Our analysis hinges upon Scopus data spanning a time period of 30 years (1991–2020) and involves a total of 969,835 researchers and 8,412,543 publications. Our contribution is two-fold. First, we characterize mobility as a fairly common phenomenon in CS, we highlight a strong correlation with standard bibliometric indicators at all seniority levels and a lower propensity of female researchers to relocate internationally than their male colleagues. Second, we analyze individual career paths building from them a mobility graph and identifying common patterns, such as the most traveled connections between different countries, whether they are equally traversed in both directions and the most frequently visited countries. The temporal evolution of the above patterns within our 30-year time frame is also investigated. The United States emerged as a preferred destination for internationally mobile authors, with strong connections to China (from the early 2000s), Canada, and several prominent European countries, most notably the United Kingdom, Germany, and France.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".