Bibliographic record
Abstract
Abstract This article explores how the Pakistan television drama industry mediates collective notions of piety through visual registers. Explicit religious discourse is tightly regulated in the industry, and producers themselves often disavow producing religious content. However, the leakiness of production practices generates religious visual idioms that are transparently circulated and taken up by audiences. Drawing from ethnographic fieldwork in Karachi with production teams in this culture industry, I argue that dramas are a central yet overlooked feature of religious publics’ formations in the digitalizing Pakistani mediascape. Focus on religious media in the anthropology of Islam has treated publics as mostly engaged with traditional sources of authority. Attending to scenes from three popular dramas—Meri Zaat Zarra-e-Benishan (2009), Shehr-e-Zaat (2012), and Khaani (2017)—elucidates how visuality is a central facet of how cross-media interactions enregister piety. Observations of cinematographic negotiations and reflections by creators on the ambiguity and efficacy of pious visuality contextualize how religious scenes in these productions come together. While the visuality of prayer scenes across these dramas emphasizes private personal piety, tracing how these images are scripted, depicted, and circulated online offers insights into how religious digital publics are shaped in contemporary Pakistan.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".