SECULARISM, FEMINISM, AND ISLAMOPHOBIA: A STUDY OF ANTI-VEILING LAWS IN FRANCE AND QUEBEC
Bibliographic record
Abstract
Anti-veiling laws require Muslim women to un-cover parts of their bodies in order to work, go to school, or even walk in public space. Since 2004, French-style anti-veiling laws have been debated and enacted globally, including in Quebec, Canada. My research asks: How and why have anti-veiling laws been enacted in both France and Quebec? How have anti-veiling laws circulated transnationally between these two sites? What are the impacts of anti-veiling laws on Muslim women who practice veiling in France and Quebec? Using a qualitative approach, I spent nine-months conducting fieldwork research in Paris and Montreal between 2012 and 2014. I interviewed 47 Muslim women who currently, previously, or periodically wore a headscarf or face-veil, and/or who identified as activists who opposed anti-veiling laws. To analyse my data, I used Saidian citational analysis alongside a transnational feminist and critical race theoretical framework. The dissertation shows that political leaders in both France and Quebec used anti-veiling laws as a legal-political strategy to solidify their national identities around la nouvelle lacit, an identity-based secularism that takes Islam, rather than Catholicism, as its main interlocutor. It also shows how a number of politicians, feminists, and media purveyors facilitated the circulation of anti-veiling laws between France and Quebec by sharing common assumption, lexicons, knowledge, and expertise, and by forming powerful networks through traveling, organizing conferences, and writing books. My findings also demonstrate that anti-veiling laws increased Islamophobia in both France and Quebec, prompting veiled Muslim women to develop survival strategies to mitigate its impacts on their everyday lives. Survival strategies included changing the way they dressed; changing their jobs or studies; starting their own associations or businesses; withdrawing from society; engaging in political/feminist activism; and finally, migration (hijra). My findings suggest that instead of promoting secularism and gender equality, anti-veiling laws negatively impact Muslim womens education and employmentforcing them to choose between their religion and their daily survival. Their migration away from France/Quebec may also exacerbate labour shortages in sectors that require highly-skilled workers. Finally, I discuss threats to democratic minority rights that anti-veiling laws enable, including ongoing legal challenges to them.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".