Ethnic and Linguistic Minorities and Political Participation in Europe
Bibliographic record
Abstract
Understanding the political behavior of ethnic minorities is important for their integration in contemporary European societies. We compare the political participation rates of ethnic and linguistic minorities to those of the majority population using data from the 2002—03 European Social Survey which covers 21 countries in Europe. Using a broad index of participation, we show that the differences between minority and majority groups are virtually zero. Only voting in national elections displays a gap between majorities and minorities. Based on a multivariate model we estimate that a person with a minority background both with reference to ethnicity and language has an 80 percent probability of voting in national elections compared to 89 percent for a person in the majority population. In making sense of these findings we have to remind ourselves that ethnic and linguistic minorities in Europe are groups that show great heterogeneity, and that not all characteristics of these groups should lead us to expect them to be less active than majority groups in every single act of political participation. This finding is in line with the emphasis of variation and heterogeneity in effects of ethnicity and language that Anderson and Paskeviciute (2006) have found in research based on aggregate indicators.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".