Bibliographic record
Abstract
The contributions in this volume explore a wide variety of humour practices related to Islamic/Muslim contexts. Egyptian TV satire in the wake of the Arab Spring is the subject of Moutaz Alkheder’s chapter on Bassem Youssef. Chourouq Nasri explores cartooning as a popular comical artform in the Arab world, examining how cartoonists navigate boundaries on comical licence. Ethnic joke cycles in Iran are documented in Fatemeh Nasr Esfahani’s chapter, addressing both their conventional ethnic thematic and their religious subversiveness. The Qur’an, too, provides material for smiles, and Yasmin Amin discusses a plethora of deliberate appropriations (and misappropriations) of Qur’anic verses and passages (so-called iqtibās) for humorous effect. Joking as an anti-fundamentalist weapon – especially aimed against ISIS – is employed from both within the Arab world and from the West, as illustrated in Mona Abdel-Fadil’s chapter. Joseph Alagha explores roles played by levity in Hizbullah’s cultural programme, noting Hizbullah-internal disagreements about the status of humour. Shifting from humour expressions in the Muslim world to comedy by Muslims in North America, Jaclyn Michael explores the boldly uninhibited female Muslim stand-up acts in America, analyzing their take on gender, sexuality, and race. Jay Friesen looks closely at the successful Canadian TV series Little Mosque on the Prairie, finding that conventional comical sitcom strategies take precedence over any putative Muslim comical sensibilities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.260 | 0.131 |
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".