Aukštos kvalifikacijos darbuotojų migracija ir darbo rinkos tvarumo užtikrinimo politikos Europos Sąjungoje 2013-2014 m.
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.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".