ROMANIA - Migration and demographic patterns in Central-Eastern Europe
Bibliographic record
Abstract
This report is a part of deliverable “D.6.2. Report on migration and demographic patterns in the EU CEE countries and potential source countries” from the project FUME – Future Migration Scenarios for Europe (870649), financed with the Horizon 2020 programme. In particular, this country report focuses on critical analysis of migration data from Romania, and in particular - immigration data. The analysis consists of an overview of stock and flow data on migrants including such dimensions as age groups, gender, country of origin and length of residence. This report is a first step in analytical exercise which aims to determine migration potential from and to Romania and furthermore, to provide necessary data input for fine-tuning of FUME migration projection model. Romania has been among the largest emigration countries in southeastern Europe for the last couple of decades. Socio-economic and political transformation brought many challenges and led to a number of waves of Romanian emigrants who settled mainly in Western and Southern Europe, but also in the United States and Canada. Recent migration patterns were largely determined by the process of European integration which brought new mobility and work opportunities. According to recent official statistics, the number of emigrants since 2011 has been higher than immigrants, and is relatively stable. On the other hand, immigration to Romania is still not significant, although it is on the increase in the last couple of years. Among new trends in immigrations is the arrival of Asian workers, mainly from China or Vietnam, which is a response to a lack of workforce in Romania. This report presents the historical framework of migration transformation, diaspora dynamics and major national groups of immigrants and their demographic structure. It also provides critical analysis of the validity of the main statistical data sources, and draws some conclusions on migration patterns and trends in Romania.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".