A cross-country configurational approach to international academic mobility: exploring mobility effects on academics’ career progression in EU countries
Bibliographic record
Abstract
Abstract This study takes a novel perspective on mobility as career script compliance to explore the factors that might influence how mobile academics in a country perceive the impact of international mobility on their overall academic career progression and job options. We conduct a country-level qualitative comparative analysis on a sample of 24 European Union (EU) countries, based on data from European Commission’s MORE3 indicator tool. We find that these perceptions about the impact are shaped by the dominant patterns of mobility in that country, and the general perception of academics in that particular country that international mobility is rewarded in the institutional promotion schemes. This study introduces new explanatory factors for the career script for international mobility. In so doing, we provide a richer understanding of how countries might influence academics’ mobility, which sheds light on previous inconclusive empirical evidence linking international mobility and academics’ careers. Our findings have implications for the policy design of international mobility and open up new lines of inquiry for cross-country comparisons.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".