The EU Policy to Attract Highly Skilled Workers: The Status of Implementation of the Blue Card Directive
Bibliographic record
Abstract
A worldwide shortage of about 38-40 million highly skilled workers is forecast by 2020. Many countries are implementing policies to attract workers with special skills and knowledge. What is the European Union doing to face this challenge? In 2009 the EU adopted the so called Blue Card Directive (n. 2009/50) to attract highly qualified workers from abroad, address labour and skills shortages and strengthen the EU’s competitiveness and economic growth. The Directive was implemented by most EU countries during 2012 but has proven to be insufficiently attractive and underused, with only a limited number of Blue Cards issued. For these reasons, the EU Commission has announced some proposed changes to the Blue Card Directive. The specific objectives are, amongst other things, to increase the numbers of third-country highly skilled workers immigrating to the EU and simplify and harmonise admission procedures for third-country highly skilled workers. The article also outlines a summary of the current state of implementation of the Directive in the following countries: Italy, France, Spain, Germany, Poland, Hungary, Austria, Belgium and The Netherlands.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".