(Re)scaling Governance of Skilled Migration in Europe: Divergence, Harmonisation, and Contestation
Bibliographic record
Abstract
ABSTRACT The European Commission has attempted to create a common European migration policy since the mid‐1980s. It has made progress in the harmonisation of asylum and family law, and the Schengen agreement has opened internal borders within the European Union (EU). But the commission's attempts to establish common admission standards for non‐EU labour migrants met with considerable opposition from member states. This paper investigates the construction, negotiations, and contestations of scales of decision‐making power in Europe, especially regarding skilled migrants. The paper first provides a short historical overview of initiatives of the European Commission to streamline migration policies across the EU, followed by a case study of the (re)scaling of the European Blue Card. The European Commission designed this initiative to attract more skilled workers to the EU. Several EU member states rejected the initial proposal to safeguard their sovereign decision‐making power. The findings of this case study indicate that the scale of the nation state remains powerful in the admission of non‐EU workers and that institutions at higher geographical scales do not necessarily dominate lower scales. The findings also show that overlapping and intertwining scales of decision‐making power hamper efforts to create a common European skilled migration policy. The newly adopted Lisbon Treaty may supersede these scales and facilitate more far‐reaching skilled migration policies. The findings of this paper contribute to debates about the rescaling of decision‐making power in the EU and the changing roles of nation states, institutions, and supranational organisations in the governance of skilled migration. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".