Moving Beyond Facial Equality: Examining Canadian and French Niqab Bans
Bibliographic record
Abstract
We should not coerce those we wish to liberate. And it is particularly offensive to advance our fight on the backs of those women who are now among the most marginalized and whose access to the workplace is the best guarantee of both their autonomy and their integration. Calls by politicians to ban the niqab or burqa have become commonplace. Muslim women who cover themselves using face veils, though relatively small in number, have managed to become front-page news in many jurisdictions and have regularly captured the global imagination. French President Nicolas Sarkozy stated in front of parliamentarians that the burqa is not welcome in France. Canadian Prime Minister Stephen Harper said that the niqab was “anti-women” and that it was “offensive that someone would hide their identity” ; “[it's] not the way we do things here.” The Australian Prime Minister stated that he found the burqa “a fairly confronting form of attire and frankly I wish it weren ‘ t worn.” In the United States, Germany and Canada, Muslim women have asked courts to recognize their right to wear the niqab in a variety of contexts. The niqab has been criminalized nationally in eight countries (France, Belgium, Bulgaria, Austria, Denmark, the Netherlands, Chad and the Congo), with partial or regional bans in Canada, Italy, Switzerland, Germany, Spain, Egypt, Syria, China, Cameroon, Niger and Algeria. This chapter builds on work already written about Muslim women who wear some sort of face veil and the ongoing agitation that has been expressed about them. I have argued elsewhere that the depth of discomfort evoked by these women and their outward markers of religiosity have resulted in a wide range of mostly specious rationalizations as to why these items of clothing must be banned. Logic appears not to be a measure used when managing issues involving women who cover their faces. Rather, one sees a predisposition, often unstated, toward linking such modest clothing to radical and violent interpretations of Islam. Where such explanation is implausible, a strident dislike of the niqab manifested as an affront to national values is articulated as though that is explanation enough for the curtailment of rights. No further justification is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".