Migration of Highly Qualified Workers and Policies to Ensure Labour Market Sustainability in the European Union in 2013-2014
Bibliographic record
Abstract
European Union is facing challenges of ageing societies and changes in structure of economy, thus labour shortages turn into an urgent issue that ultimately affects labour market sustainability. In its attempt to recruit highly qualified workers EU has strong international competitors, e.g. USA, Canada, Australia, New Zealand, and pursues a variety of initiatives at national level of the Member States and at the EU level in general. This article aims at assessing the EU policies related to migration of highly qualified workers. Statistical data analysis has revealed that labour mobility is increasing in EU. Thus the EU Mobility directive could be evaluated as bringing benefits, yet with a room for improvement, because highly qualified workers still make up just a small part in all the mobile citizens’ population. National initiatives are more effective in fostering the migration of highly qualified workers, but this has the threat of unequal benefits in different EU regions; the effectiveness of EU Blue Card initiative is weak but with a high potential, thus it needs further improvements in its issuing policies. DOI: http://dx.doi.org/10.5755/j01.ppaa.16.3.19345 An erratum to this article is available at: http://vpa.ktu.lt/index.php/PPA/article/view/24731
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".