Cross-border labour mobility within an enlarged EU
Bibliographic record
Abstract
This paper examines the potential for increased cross-border labour mobility within the EU-25 and considers the costs and benefits of any increase in labour mobility to both sending and receiving countries in the medium to long run. Evidence from previous EU enlargement experiences, academic studies, the existence of barriers to mobility within the EU and the economic determinants of migration all indicate a moderate potential for increased migrant flows. The magnitude of cross-border labour flow in the medium to long run will most likely be largely a function of the demand for migrants and the speed at which the EU-8 catches up economically with the EU-15. In addition, faster population ageing in the EU-8 tends towards dampening migration flow from the new Member States in the medium term. In terms of costs and benefits, for the EU-8 countries labour migration, especially in the short run, may present a number of challenges. Emigration may tend to weigh disproportionally on the pool of young and educated workers, aggravating labour market bottlenecks. For the EU-25 as a whole, cross-border labour mobility is likely to offer a number of advantages, by allowing a more efficient matching of workers' skills with job vacancies and facilitating the general upskilling of European workforces. The current restrictions on labour mobility from the EU-8 countries to the other EU member countries stand in contrast with one of the central principles of the EU - the free movement of labour. Furthermore, these restrictions hamper an important adjustment mechanism within EMU. Delaying the removal of these barriers may be costly for the EU-25 at a time when leaders are concerned about Europe's international competitiveness. Finally, it would not be beneficial for Europe to loose a significant part of the most agile and talented individuals from the new Member States to more traditional migration centres.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".