Labour Mobility in the Enlarged Single European Market
Bibliographic record
Abstract
Introduction: transnational labour mobility - engine for social convergence or divergence in Europe? / Jon Erik Dlvik -- New patterns of labour migration from Central and Eastern Europe and its impact on labour markets and institutions in Norway: reviewing the evidence / Jon Horgen Friberg -- Policy response to emigration from the Baltics: confronting 'the European elephant in the room' / Indre Genelyte -- Sectoral variation in consequences of intra-European labour migration: how unions and structural conditions matter / Bjarke Refslund -- A Canadian immigration model for Europe? labour market uncertainty and migration policy in Canada, Germany, and Spain / Guglielmo Meardi, Antonio Martín Artiles, Axel van den Berg -- Move to work, move to stay? mapping atypical labour migration into Germany / Bettina Wagner, Anke Hassel -- Freer labour markets, more rules? how transnational labour mobility can strengthen collective bargaining / Alexandre Afonso -- East-West mobility and the (re-)regulation of employment in transnational labour markets / Torben Krings -- Locked in inferiority? the positions of Estonian construction workers in the Finnish migrant labour regime / Markku Sippola, Kairit Kall
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".