Technologies of Self-Wrapping: Female Chanters in the Fayḍa Tijāniyya Sufi Community in Senegal
Bibliographic record
Abstract
The prevalent conception in many Muslim communities globally that women’s visibility must be minimized or attenuated in the presence of unrelated men profoundly shapes Muslim women’s relationship to visibility. Many Muslim women participate in and influence their communities through forms of “wrapping”—a semiotic act that covers and protects yet also identifies and displays. The concept of “wrapping” encompasses “veiling” yet moves beyond clichés of invisible and silenced Muslim women. In the Fayḍa Tijāniyya Sufi community in Senegal, female Sufi chanters were until recently practically unknown, largely due to the perception that a woman’s voice—like her body and social presence—is ʿawra, or something to be cloaked and protected. Since around 2009, however, female chanters have proliferated, some becoming online superstars and acting as formally appointed spiritual guides (muqaddamas). These women largely embrace the notion of a woman’s voice and body as ʿawra, yet they adopt various social and material technologies as “wrappers” that mediate their chanting before large audiences. Female chanters exemplify the dialectic in the Sufi tradition—between the flexibility associated with transcendent reality (ḥaqīqa) and the limits associated with divine law (sharīʿa)—which facilitates yet constrains adaptation to changing historical conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".