“We are all republicans”: Political Articulation and the Production of Nationhood in France's Face Veil Debate
Bibliographic record
Abstract
Abstract In July 2010, following a year-long nationwide debate over Islamic veiling, the French government passed a law prohibiting facial coverings in all public spaces. Prior research attributes this and other restrictive laws to France's republican secular tradition. This article takes a different approach. Building on literature that sees electoral politics as a site for articulating, rather than merely reflecting, social identities, I argue that the 2010 ban arose in significant part out of political parties’ struggles to demarcate the boundaries of legitimate politics in the face of an ultra-right electoral threat. Specifically, I show that in seeking to prevent the ultra-right National Front party from monopolizing the religious signs issue, France's major right and left parties agreed to portray republicanism as requiring the exclusion of face veiling from public space. Because it was forged in conflict, however, the consensus thus generated is highly fractured and unstable. It conceals ongoing conflict, both between and within political parties, over the precise meaning(s) of French republican nationhood. The findings thus underscore the relationship between boundary-drawing in the political sphere and the process of demarcating the cultural and political boundaries of nationhood in contexts of immigrant diversity.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.027 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".