Rethinking the attractiveness of EU labour immigration policies : comparative perspectives on the EU, the US, Canada and beyond
Bibliographic record
Abstract
Is Europe's immigration policy attractive? One of the priorities driving current EU debates on labour immigration policies is the perceived need to boost Europe's attractiveness vis-á-vis 'talented' and 'highly skilled' immigrants. The EU sees itself playing a role in persuading immigrants to choose Europe over other competing destinations, such as the US or Canada. This book critically examines the determinants and challenges characterising discussions focused on the attractiveness of labour migration policies in the EU as well as other international settings. It calls for re-thinking some of the most commonly held premises and assumptions underlying the narratives of ‘attractiveness’ and ‘global competition for talent’ in migration policy debates. How can an immigration policy, in fact, be made to be ‘attractive’ and what are the incentives at play (if any)? A multidisciplinary team of leading scholars and experts in migration studies address the main issues and challenges related to the role played by rights and discrimination, qualifications and skills, and matching demand and supply in needs-based migration policies. The experiences in other jurisdictions such as South America, Canada and the United States are also covered: Are these countries indeed so ‘attractive’ and ‘competitive’, and if so what makes them more attractive than the EU? On the basis of the discussions and findings presented across the various contributions, the book identifies a number of priorities for policy formulation and design in the next generation of EU labour migration policies. In particular, it highlights important initiatives that the new European Commission should focus on in the years to come.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".