Integration policies and threat perceptions following the European migration crisis: New insights into the policy-threat nexus
Bibliographic record
Abstract
The link between integration policies and intergroup attitudes or threat perceptions has received considerable attention. However, no studies so far have been able to explore how this relationship changed following the European migration crisis due to a lack of recent comparative policy data. Using new MIPEX data, this is the first study to examine mechanisms underlying the policy-threat nexus following the European migration crisis, distinguishing between several strands of integration policies, and realistic and symbolic threat. To do so, we combine 2017 Eurobarometer data with 2017 Migrant Integration Policy data, resulting in a sample of 28,080 respondents nested in 28 countries. The analyses also control for economic conditions, outgroup size, and media freedom. Multilevel analyses indicate that respondents living in countries with more inclusive integration policies in general report lower realistic and symbolic threat. When investigating different policy strands, we find that inclusive policies regarding political participation and access to nationality for immigrants are associated with lower realistic and symbolic threat. We compare our findings to those from prior to the European migration crisis and discuss the potential role of this crisis in the policy-threat nexus.
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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 | 0.004 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".