HIGHLY SKILLED MIGRATION AS A SOURCE AND A CHALLENGE FOR COMPETITIVENESS OF STATE
Why this work is in the frame
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Bibliographic record
Abstract
The paper reviews the growing impact of highly skilled migration policies on the competitiveness of states. Highly skilled migrants (HSM) are regarded as valuable contributors to the knowledge economy, that the receiving countries are competing with each other for. The increase in HSM number (arriving with an H1B visa) had a positive effect on innovative development at macro and micro levels in the United States. A significant role in creation of innovations is played by foreign students, in particular those studying on the STEM (Science, Technology, Engineering and Mathematics programs). At the same time, an overall contribution of HSM to the innovation development of host countries is much greater than the number of patents, grants and highly cited publications, given indirect effects of immigration which play an equally important role in creating innovations: “the effects of knowledge spillover” from immigrants to colleagues. The author gives an overview of a range of foreign studies which demonstrate a strong positive impact of HSM on creating of innovations, and analyses some successful national approaches to HSM selection (cases of the USA, Australia and Canada). In recent years, Russian government has introduced a set of initiatives in migration politics, aiming at HSM. However, there is still a lack of sufficient public discussion on benefits HSM can bring to the Russian economy. Besides, low attractiveness of Russia for HSM challenges its capacity to compete with the leaders in a “global race for talents”, and therefore, to manage highly skilled migration policy as a source for innovation development. Universities, research institutes and high-technology firms serve as the main centers of innovation creation and attraction for HSM. Therefore, high-skilled migration policy should focus on the involvement of these recruiters through strengthening the internationalization and competitiveness of Russian universities, R&D and business sector. Moreover, the policy will have a highly positive effect if HSM represent different cultures. This implies the necessity to elaborate and introduce effective multicultural practices in education, research and business activities.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it