Bibliographic record
Abstract
This article is an attempt to analyze the growing agitation that has been expressed about Muslim women who cover their faces. I trace North American and European contexts in which the issue of the veil is a site of social debate and contestation by canvassing the media and law reports from the last five years. The depth of discomfort evoked by these women and their outward markers of religiosity is extraordinary and as I will demonstrate results in a wide range of rationalizations as to why their public displays of religiosity must be banned. Part I of this paper describes various explanations as to why some Muslim women cover parts of their bodies. However, the main purpose of this article is to examine opposition to the niqab. Thus part II of this paper critically examines ten arguments for why women should not wear the niqab. The focus of this article, on opposition to niqab-wearing women in public spaces, is not to further marginalize an already beleaguered minority. Rather, it is to critically unpack arguments that insist on alienating a religious minority such that the refocusing of the gaze is on “us”, on the reasons we offer to exclude certain people from social and political life. That the plight of niqab-wearing women might help us better understand ourselves is the ultimate objective of this paper.
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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 | 0.006 | 0.014 |
| 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 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| 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".