Innovation development and migration: panel data approach
Bibliographic record
Abstract
The intellectual capital is a catalysator of the country’s economic growth. The developed countries try to develop attractive conditions for highly qualified migrants to diffuse the knowledge and innovations. The authors provided the bibliometric analysis of the papers, which focused on the analysis of the migrant issues was done. For the bibliometric analysis, the metadata of 2 500 papers was selected from Scopus. The results showed that the numbers of Scopus documents on the allocated theme have increased for 2015 year. The most powerful investigations were provided by scientists from the USA, Canada, France, United Kingdom. The bibliometric analysis findings confirmed that the scientists allocated a vast range of the determinants that could stimulate or restrict the migrants in the country. Thus, the governance efficiency had the mediation role between the migration and innovation development of the country. In this case, the paper aims to check the hypothesis that the increasing (decreasing) level of country innovation development and government efficiency from year t − 1 to year t positively (negatively) affects net migration in year t + 1. The panel data for 2011-2018 was generated from IndexMundi, EU Data Portal, WorldBank. The object of the investigation was Bulgaria, Croatia, Lithuania, Latvia, Poland, Romania. The dependent variables – net migration rate, the independent variables – World Government Indicators: Control of Corruption, Government Effectiveness, Political Stability, Rule of Law, Regulatory Quality, Voice and Accountability (for assessment of government efficiency), Innovation Index (for assessment of country's innovation development). In the paper, to check the hypothesis, the authors used the Fully Modified Ordinary Least Square for homogeneous and heterogeneous models. The findings confirmed that innovation development and governance efficiency (Political Stability and Absence of Violence/Terrorism, Regulatory Quality, Voice and Accountability) had a statistically significant impact on the migration rate. The findings could be used to identify the strategic goals of innovation development to overcome the demographic issues and support the migration of the high qualified workforces.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".