Bibliographic record
Abstract
In this article, I consider how the racialisation of Muslim identities in the French context affects the education and employment trajectories of six young French Muslim women with post-secondary education, living and working in Paris. I call attention to the pernicious effects of the intersection of three sets of governing discourses: laïcité, post-feminism and neoliberalism. These discourses obscure the way state-endorsed racialisation intersects with class and gender relations to erect barriers to Muslim women's employment opportunities. I examine the complex discursive and performative work Muslim women engage in, to inhabit, reproduce, reject or contest various interpretations of pious feminine Muslim and of French secular republican subjecthood. Work sites become important places where both pious and laïque subjectivities are often simultaneously produced and negotiated through performance and corporeality. In this way, the women's narratives challenge the discursive construction of the incompatibility of pious and secular subjectivities. Participants disrupted their racialisation as oppressed women who embody Muslimness by emphasising their individual and conscious choice to practise their religion. Yet, in doing so, and in the light of the challenges finding work for those wearing the headscarf, they were inadvertently rendered the agents of the discriminatory treatment that disadvantaged them in the labour market. The rational, free-choosing, neoliberal ‘self’ that they construct must then take individual responsibility for the negative consequences on their lives of broader collective racialising discourses.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".