Aukštos kvalifikacijos darbuotojų migracija ir darbo rinkos tvarumo užtikrinimo politikos Europos Sąjungoje 2013-2014 m.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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 it