Cultural Integration of First-Generation Immigrants: Evidence from European Union Countries
Bibliographic record
Abstract
In this study, we aim to explore and compare the frequency of attendance and the reasons for non-attendance to cultural activities between natives and first-generation immigrants in thirteen European countries. The empirical analysis relies on data from the special module on cultural participation in the European Union-Income and Living Conditions Survey (EU-SILC) in 2015. We apply the Probit and multinomial Probit models. This study contributes to the literature by exploring the determinants of cultural participation and comparing the frequency of participation in cultural activities between natives and first-generation immigrants. Furthermore, the study explores the reasons for non-participation in cultural activities, highlighting potential differences between countries and between the European Union (EU) and non-EU migrants. The results highlight that social interactions depend on several factors related mainly to the country of destination and employment opportunities and individual factors related to the migrant, including demographic and economic characteristics and the length of residence in the host country. The findings show that the length of residence of immigrants in the host countries is positively correlated with a higher frequency of attendance, indicating that cultural participation can be, by its nature, a long-term process or “experienced” activity. The findings also show that in most cases, migrants do not attend the cultural activities we explore because of financial constraints and not due to lack of interest. Thus, this highlights that the economic integration of migrants could be the primary driver of cultural participation and integration.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".