“You can’t forget our roots anyway”: French College Students’ views on a Racially and Religiously Pluralistic France
Bibliographic record
Abstract
Despite the longstanding presence of Islam in the territory of France, Muslim French must still claim and justify their belonging in the context of widespread public skepticism over Islam’s compatibility with “French” social and cultural values, such as laïcité, or secularism. The general public’s skepticism is also, in part, due to the historical and ongoing racialization of Muslim populations. Many French sub-populations, including those who are perceived as more “liberal” such as college students, are a part of this skeptical public. Therefore, how have these students speci cally been shaped by contemporary French discourses and understandings of laïcité? There is a lack of scholarly research on French college students in particular and their understandings of French identity, laïcité, and Muslims in France. To ll this gap, I conducted nine semi-structured interviews and drew on informal participant observation. In this article, I discuss French college students’ opinions on French identity as well as the desire for widespread assimilation, speci cally regarding Muslim women and their choice to wear a hijab in France. I examine these viewpoints within the framework of dominant French discourse, which often perpetuates the idea of a racialized Islam that is inherently incompatible with French culture. I argue that students on both the left and right sides of the political spectrum still reiterate opinions that t within this dominant French discourse.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".