Electoral Participation of Non‐National EU Citizens in France: The Case of the Nord
Bibliographic record
Abstract
Since the 1993 Maastricht Treaty, EU citizens have the right to vote in European and local elections in the member state they reside in. In France, only about a quarter do so. Our article considers what factors explain the registration and participation of non-national citizens for the French Department of the Nord where around 35,000 non-French European citizens of voting age are living. Among them, 11,638 are registered to vote in the French municipal elections. Following the 2020 municipal elections, we have consulted the electoral rolls in each of the 648 communes to know who actually cast a vote. Based on detailed census data on each EU nationality and on other information contained on the electoral lists and rolls (age, gender, place of birth, etc.) and also contextual variables, this article seeks to identify the main factors associated with registering in the first instance and turning out to vote in the second. Our results confirm wide variation in registration and voting rates according to nationality. They also show that beyond voters’ nationality and the “usual suspects” of electoral participation, contextual factors are important predictors.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".