Bibliographic record
Abstract
Recent developments in European security situation, starting with the Russia-Ukraine conflict, followed by the complicated Brexit and political instability in the Middle East and North Africa, have given rise to instability in the European Union. Yet, none of the other factors could be compared with the risks caused by the massive influx of refugees into the EU that challenges both solidarity and responsibility of the member states. In this context, it is extremely important to understand the actual security threats related to the refugee crisis and the root causes of growing refugee flows. This article discusses the roots of large-scale migration flows in the European Union (EU) over the present decade and investigates the potential link between migration flows and modern hybrid warfare, referring to the coordination of various modes of warfare, such as military and non-military means, conventional and non-conventional capabilities, state and non-state actors with an aim to cause instability and disarrangement. It is intriguing to investigate whether the increase in migration flows could be linked to present confrontation in the global arena on the Russia-West axis. Common patterns of migration flows from Syria and Ukraine to the EU are discussed, as well as policy recommendations are given to diminish the negative impact of similar events in the future.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".