Engaging Immigrants? Examining the Correlates of Electoral Participation among Voters with Migration Backgrounds
Bibliographic record
Abstract
An increasing number of eligible citizens in North America and Europe were born outside of these countries. As remarked by Heath et al. [2011. “Ethnic Heterogeneity in the Social Bases of Voting at the 2010 British General Election.” Journal of Elections, Public Opinion and Parties 21 (2): 255–277], in the case that voters with migration background respond differently to established correlates of turnout, understanding the role of immigration-specific factors becomes particularly important. On the basis of individual-level register data from the 2012 Finnish municipal elections (n = 585,839), we examine whether the effect of socioeconomic status on turnout differs according to citizenship status and test which indicators of social and political integration boost participation among foreign-born voters. We find, in line with the different response model, that the impact of age and education is weaker among voters with migration background. In addition, having a native spouse and minor children, past eligibility and being born in a democratic country increase turnout among foreign-born voters, lending support for the assimilation, exposure and transferability models. Finally, the findings concerning the resistance model were opposite to our expectations. Older age at the time of immigration increases participation, but only among migrants born in a democratic country.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".