THE EFFECTS OF THE COVID-19 PANDEMIC ON THE EXTERNAL MIGRATION OF THE ROMANIAN POPULATION
Bibliographic record
Abstract
The pandemic and its consequences have affected the lives of people all over the world. But migrants were much more affected than any other population groups. The pandemic has, in the first phase, drastically reduced migration in all OECD countries, a phenomenon noted by the Organization for Economic Cooperation and Development, although migrants would have managed to ensure the functioning of some sectors strongly affected by the pandemic, such as the health, commercial and logistic, even during the restrictions period. In the midst of the pandemic, governments took exceptional measures, which limited the mobility of people and in this case, the mobility of migrants. The OECD believes that migrants have been particularly affected by the coronavirus pandemic, and as far as migration is concerned, it has been considerably reduced, an unfavorable phenomenon for both parties: both for the countries providing migration and for those receiving migration. Many of the migrants work in gastronomy, in hotels, in tourism - so exactly in the industries that were most affected by the pandemic. In the so-called HORECA sector in the EU, about a quarter of the employees come from third countries, twice more than in the rest of the economic sectors. The work contracts in the field are often very short-term. As such, the migrants were the first to be sent into unemployment. The paper aims to present an analysis of the impact of the COVID-19 pandemic on international migration from Romania starting from the analysis of the phenomenon from the pre-pandemic period, then extending the analysis of this phenomenon for the period 2020-2021.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".