Bibliographic record
Abstract
WHAT IS VEILING? Sahar Amer Chapel Hill, NC: The University of North Carolina Press, 2014 [ILLUSTRATION OMITTED] I'd like to begin with several images personal to me (a non-Muslim) but also, I think, relevant to anyone who has ever pondered increasingly vital question What is Veiling?, so conscientiously and sensibly addressed in this book. The first covered women I encountered were nuns of my early education who epitomized mystery and severity. They continued to inspire fear and resentment until a couple of them finally humanized through their passion for literature, which became my joy. As an adult, on occasion of my first visit to Egypt in early '90s where pyramids and Valley of Kings left indelible impressions on me, what also struck were certain visuals of veiled women. Admittedly these were not numerous during Mubarak's era, which is why they stood out. I remember being in a taxi, traffic swarming around, when suddenly and imperiously from car ahead, thrust a blackgloved arm (rings on fingers) indicating a turn. The woman behind wheel was covered completely in black but her gesture was assertive, a strong combination of aggression and anonymity. Considering completeness of her covering, she was probably from Saudi Arabia, Iraq or one of Gulf States. Author Sahar Amer supplies a complete glossary of names of garments worn by Muslims worldwide that runs to 9 pages, attesting to complexity of this subject that is at once sartorial, cultural, religious, social, historical and artistic. In 2011, when invited to teach and share my own poetry at Ain Shams University in Egypt, 1 was intrigued, during a leisurely country house party, to hear an older female academic dressed casually in slacks and a blouse gently chastise her students for wearing hijab, despite fact that they seemed to compensate with heavy eye makeup and bright colours. The term hijab, Amer declares, is the generic term for veiling used by all Muslims regardless of background. She comments how, when watching Egyptian films from 1940s to 1960s, she is struck by European dress and lifestyle of women they portray. And how her mother's wedding pictures from early '60s display same western influences, this, as she points out, thirty years before they all began wearing hijab. In Turkey recently I watched a veiled woman at breakfast with her family in an Istanbul hotel. Young daughters and husband enthusiastically tucked into food while she delicately lifted her face cover and precariously balanced a piece of boiled egg on a spoon in order to get it into her mouth. She looked uncomfortable and sedately resigned. Amer reminds us that for democratic reformer, Ataturk in Turkey in 1920s, hijab was an obstacle to progress and secularization. In 2014 Anniversary edition of Canadian Woman Studies, my own prose poem Out of deals with how I felt in 2011 being constrained to wear headscarf (I have particularly unruly hair) and a myriad number of contradictory impressions around situation of women and girls in that far-reaching, richly-endowed culture. Again we remember that before revolution of 1979, Iran was a largely secular society. After it, Iranian women who had demonstrated for their right to choose to wear chador unexpectedly found themselves forced to wear it. My favourite incident occurred at end of a course I teach at York University called Women in Literature. Several of my students had worn full hijab, that is, complete covering all year. …
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.065 | 0.038 |
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".