Integration of Immigrants in European Labour Markets
Bibliographic record
Abstract
The European refugee crisis of 2015 has led to a renewed surge of research into how to integrate refugees -as well as family and labour migrants -into European societies and in particular into EU labour markets. The papers in this Forum examine previous research on immigrant integration and update it with fresh data and new insights. Why do immigrants integrate better in some countries than others? Which skills have proven to be most essential for their successful integration, and what tools are available to countries to foster this integration? This Forum combines studies of the labour market integration programmes and methods used in specifi c countries -including a "best practice" review of the Canadian immigrant integration system -with a review of social integration measures and an analysis of Bosnian refugee integration efforts from the 1990s. While the labour market integration of immigrants will likely always remain a diffi cult task, the authors in this Forum hope to contribute to improving this process and consequently enriching both host countries' economies and immigrants' lives.
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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 | 0.000 | 0.000 |
| 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".