How the Level of Economic Growth and the Constituent Elements of Innovation Attract International Talent?
Bibliographic record
Abstract
The international flow of highly skilled workers has a large effect on any country. Simultaneously innovation and its´ constituent elements allow increasing growth in a sustainable way. This paper analyses whether innovation and economic growth levels are related with the attraction of highly skilled immigrants. In order to do that a cluster analysis of 182 countries which are net exporters of highly skilled immigrants and 25 receiving countries, members of the OECD. It includes a detailed discussion of results by world regions including examples of specific programmes and policies for each variable of the study. Overall, our results confirm that the attraction of international talent is related with the constituent elements of innovation and with the level of economic growth. In this regard, countries like the USA, Australia, Canada and the United Kingdom are among the main destinations of highly skilled immigrants of any region of origin due to the implementation of policies that favour the development of innovation. However, access restrictions for highly skilled workers would limit the effects of those policies. Therefore, in the design of strategies for attracting talent, the qualification of such workers should have precedence over the country of origin of the person, encouraging more the innovation activities of highly skilled immigrants.DOI: http://dx.doi.org/10.5755/j01.ee.28.2.17518
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".