Migration and Skills : The Experience of Migrant Workers from Albania, Egypt, Moldova, and Tunisia
Bibliographic record
Abstract
The subject of migration, and how best to manage it, has been moving up the policy agenda of the European Union for some time now. Faced with an aging population, possible skills shortages at all skills levels, and the need to compete for highly skilled migrants with countries such as Australia, Canada, and the United States, the European Union (EU) is moving from seeing migration as a problem or a threat to viewing it as an opportunity. As an EU agency promoting skills and human capital development in transition and developing countries, the European Training Foundation (ETF) wished to explore the impact of migration on skills development, with a special emphasis on Diasporas and returning migrants. For the World Bank, the issue of migration forms an integral part of its approach to social protection, since it believes that labor-market policy must take into account the national as well the international dimensions of skilled labor mobility. Both institutions were keen to look at what changes need to be made to migration policy in order to achieve a triple-win situation, one that can benefit both sending and receiving countries as well as the migrants themselves. This report aims to unravel the complex relationship between migration and skills development. It paints a precise picture of potential and returning migrants from four very different countries, Albania, the Arab Republic of Egypt, Moldova, and Tunisia, that is a conscious choice of two 'traditional' (Egypt, Tunisia) and two 'new' (Albania, Moldova) sending countries, and describes the skills they possess and the impact that the experience of migration has on their skills development. It is harder to draw accurate conclusions on the link between job aspirations and current employment status, since many of the potential migrants were not actively employed at the time of the interview. However, the data suggest people did expect to change jobs as a result of migration, and the sectors they expected to work in varied according to their nationality. Focusing solely on those planning to move to the EU, many Albanians expected to work in domestic service, hospitality, and construction; Egyptians expected to work in hospitality and construction; Moldovans expected to work in domestic service and construction; and Tunisians expected to work in hospitality and manufacturing. Few migrants working in agriculture or petty trade aimed to work in these same sectors while abroad.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".