A Nation's Dilemma: Party Politics and the Production of Nationhood, Belonging and Citizenship in France's Face Veil Debate
Bibliographic record
Abstract
In April 2011 – following a two-year-long nationwide debate over Islamic veiling – the French government implemented a law that prohibits facial coverings in all public spaces. Prior research attributes this and other restrictive laws to France’s republican secular tradition. This dissertation takes a different approach. Building on literature that sees electoral politics as a site for generating – rather than merely reflecting – societal meanings, it argues that the 2011 ban arose in significant part out of political parties’ struggle to demarcate the boundaries of the electoral sphere in the face of an ultra-right electoral threat. Specifically, it shows 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 agreement thus generated is highly fractured and unstable. It also conceals ongoing conflict, both within political parties and in civil society, over the precise meaning of French republicanism. 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, belonging and citizenship 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.034 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".