L'Union européenne et l'immigration économique : les défis d'une gouvernance multi-niveaux
Bibliographic record
Abstract
The intervention of the European Union in the field of labour migration is facing a tension between federalisation and decentralisation. While cooperation on migration matters is needed, Member States are reluctant to act in common when it comes to labour migration. Labour migration is indeed linked to socio-economic parameters which are different from one country to another, and it is a sensitive field for national sovereignties. This raises the following questions: what should the EU do in the field of labour migration? Where is there an added value compared to national policies? In my doctoral research, I'm first looking at the origins and the purposes of the EU competence in the field of labour migration. In a second part, I look at secondary law and whether EU law has achieved any added value in conformity with the principle of subsidiarity. The third part is more prospective and based on a camparison with Canada where immigration is also a shared competence between the federal state and the provinces. It discusses potential actions that could overcome the tension between federalisation and decentralisation.
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.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".