European Union Migration Policies for the Highly Skilled: A Critical Appraisal
Bibliographic record
Abstract
There have been many scholarly discussions if EU can be a global power or not. Global power normatively has to have certain conditions and properties. Being a great military power would not make a country become a global power, in contrast with what some scholars argue. A global power also has the best of the brains, is attractive to researchers, provides freedom of expression and leaves space for creativity as much as gives the tools for novelty. All these cases might be provided but access to them might be limited by the immigration policies. Even though the EU and member countries in particular have achieved great accomplishments in their migration policies for the highly skilled, they do not seem to be sufficient and farsighted. The perspective towards immigration affects the whole approach to the high skilled: promoting circular migration, difficulty in transitions to permanent statuses and lack of consideration of skill losses. It is assumed that the high-skilled migrants automatically integrate and so integration policies are not devised specifically for them. It is also believed that they would bring the know-how to their home countries as a result of return migration but many of them after four or five years, hesitate to turn back to their homes. They would rather lead transnational lives. These details are not considered in EU migration policy for the highly skilled. I suggest that the EU policies on high-skilled migration should be combined with other integration policy tools. Otherwise, EU will remain behind the traditional brain attracting countries such as Australia, Canada and the USA and will never be a full-fledged global power. I tried to answer this question indeed: are the high-skilled migration policies of the EU sufficient in their design to turn EU into a global power? The answer is “no” and I explain why in this paper.
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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 | 0.055 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".