What Explains Differences in Immigration Policy in Today’s Europe? Germany, Sweden and Hungary
Bibliographic record
Abstract
This paper examines how national identity, geopolitical factors, and the 2015/16 refugee crisis have shaped immigration policies in Germany, Sweden, and Hungary, highlighting the broader European divide on immigration. Germany, known for its humanitarian values, initially adopted an open-door policy under Chancellor Merkel but later shifted toward more restrictive measures due to rising populism, security concerns, and resource limitations, particularly through the 2020 Immigration Act. Sweden, once a model of liberal immigration policies, faced challenges in social cohesion and integration, leading to a tightening of asylum rules and family reunification restrictions. In contrast, Hungary, under Prime Minister Viktor Orbán, has maintained a hardline anti-immigration stance, using the crisis to reinforce Hungary’s national identity and reject EU solidarity efforts. The paper also explores the EU’s principle of solidarity, as outlined in Article 80 of the Treaty on the Functioning of the European Union, which calls for fair burden-sharing. However, the Visegrád Group’s resistance highlights how national interests often outweigh EU-wide agreements, revealing the limits of EU cohesion and the complexities of balancing national security, identity, and humanitarian obligations. The paper concludes by advocating for more flexible immigration policies that can address both domestic and broader geopolitical challenges.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".